{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8df7fe9b-87ac-4027-89f8-d27a91f7b361",
   "metadata": {},
   "source": [
    "### 需求分析\n",
    "1. 销量分析\n",
    "2. 用户角度分析（购买量、消费金额、次数、复购率、回购率）\n",
    "3. 用户分层\n",
    "4. 用户生命周期、留存率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd5fb2c6-edbe-47ab-8e1c-f8ebe2a417c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f493e89-fbed-4e3f-bcc8-3821e5337053",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8757 entries, 0 to 8756\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   author     8757 non-null   object\n",
      " 1   rating     8757 non-null   int64 \n",
      " 2   time       8757 non-null   object\n",
      " 3   year       8757 non-null   int64 \n",
      " 4   amount     8757 non-null   int64 \n",
      " 5   frequency  8757 non-null   int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 410.6+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>rating</th>\n",
       "      <th>time</th>\n",
       "      <th>year</th>\n",
       "      <th>amount</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YOUNG</td>\n",
       "      <td>60</td>\n",
       "      <td>2019/2/28</td>\n",
       "      <td>2019</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SHING YAN</td>\n",
       "      <td>100</td>\n",
       "      <td>2019/2/28</td>\n",
       "      <td>2019</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sha</td>\n",
       "      <td>60</td>\n",
       "      <td>2019/2/28</td>\n",
       "      <td>2019</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mary Mae</td>\n",
       "      <td>100</td>\n",
       "      <td>2019/2/28</td>\n",
       "      <td>2019</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maria Cristina</td>\n",
       "      <td>100</td>\n",
       "      <td>2019/2/28</td>\n",
       "      <td>2019</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>Jonathan</td>\n",
       "      <td>100</td>\n",
       "      <td>2016/8/6</td>\n",
       "      <td>2016</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8753</th>\n",
       "      <td>Oliver Stephen Ah Kam</td>\n",
       "      <td>100</td>\n",
       "      <td>2016/8/5</td>\n",
       "      <td>2016</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8754</th>\n",
       "      <td>Halley</td>\n",
       "      <td>100</td>\n",
       "      <td>2016/8/5</td>\n",
       "      <td>2016</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>ANDREW WEIQIANG</td>\n",
       "      <td>100</td>\n",
       "      <td>2016/8/3</td>\n",
       "      <td>2016</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>WEI CHIEH</td>\n",
       "      <td>60</td>\n",
       "      <td>2016/8/2</td>\n",
       "      <td>2016</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8757 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     author  rating       time  year  amount  frequency\n",
       "0                     YOUNG      60  2019/2/28  2019     110          1\n",
       "1                 SHING YAN     100  2019/2/28  2019     110          1\n",
       "2                       Sha      60  2019/2/28  2019     110          1\n",
       "3                  Mary Mae     100  2019/2/28  2019     110          1\n",
       "4            Maria Cristina     100  2019/2/28  2019     110          1\n",
       "...                     ...     ...        ...   ...     ...        ...\n",
       "8752               Jonathan     100   2016/8/6  2016     110          1\n",
       "8753  Oliver Stephen Ah Kam     100   2016/8/5  2016     110          1\n",
       "8754                 Halley     100   2016/8/5  2016     110          1\n",
       "8755        ANDREW WEIQIANG     100   2016/8/3  2016     110          1\n",
       "8756              WEI CHIEH      60   2016/8/2  2016     110          1\n",
       "\n",
       "[8757 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "\n",
    "df = pd.read_csv('data/kelu.csv')\n",
    "print(df.info())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a649f74c-56d9-4329-b8e0-8d4aa78950ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     author  rating       time  year  amount  frequency  \\\n",
      "0                     YOUNG      60  2019/2/28  2019     110          1   \n",
      "1                 SHING YAN     100  2019/2/28  2019     110          1   \n",
      "2                       Sha      60  2019/2/28  2019     110          1   \n",
      "3                  Mary Mae     100  2019/2/28  2019     110          1   \n",
      "4            Maria Cristina     100  2019/2/28  2019     110          1   \n",
      "...                     ...     ...        ...   ...     ...        ...   \n",
      "8752               Jonathan     100   2016/8/6  2016     110          1   \n",
      "8753  Oliver Stephen Ah Kam     100   2016/8/5  2016     110          1   \n",
      "8754                 Halley     100   2016/8/5  2016     110          1   \n",
      "8755        ANDREW WEIQIANG     100   2016/8/3  2016     110          1   \n",
      "8756              WEI CHIEH      60   2016/8/2  2016     110          1   \n",
      "\n",
      "           date year_month  \n",
      "0    2019-02-28    2019-02  \n",
      "1    2019-02-28    2019-02  \n",
      "2    2019-02-28    2019-02  \n",
      "3    2019-02-28    2019-02  \n",
      "4    2019-02-28    2019-02  \n",
      "...         ...        ...  \n",
      "8752 2016-08-06    2016-08  \n",
      "8753 2016-08-05    2016-08  \n",
      "8754 2016-08-05    2016-08  \n",
      "8755 2016-08-03    2016-08  \n",
      "8756 2016-08-02    2016-08  \n",
      "\n",
      "[8757 rows x 8 columns]\n",
      "            rating         year  amount  frequency  \\\n",
      "count  8757.000000  8757.000000  8757.0     8757.0   \n",
      "mean     92.417495  2017.760420   110.0        1.0   \n",
      "min      20.000000  2016.000000   110.0        1.0   \n",
      "25%      80.000000  2017.000000   110.0        1.0   \n",
      "50%     100.000000  2018.000000   110.0        1.0   \n",
      "75%     100.000000  2018.000000   110.0        1.0   \n",
      "max     100.000000  2019.000000   110.0        1.0   \n",
      "std      14.231179     0.686734     0.0        0.0   \n",
      "\n",
      "                                date  \n",
      "count                           8757  \n",
      "mean   2018-04-20 00:24:30.092497664  \n",
      "min              2016-08-02 00:00:00  \n",
      "25%              2017-11-11 00:00:00  \n",
      "50%              2018-06-09 00:00:00  \n",
      "75%              2018-10-30 00:00:00  \n",
      "max              2019-02-28 00:00:00  \n",
      "std                              NaN  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8757 entries, 0 to 8756\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   author      8757 non-null   object        \n",
      " 1   rating      8757 non-null   int64         \n",
      " 2   time        8757 non-null   object        \n",
      " 3   year        8757 non-null   int64         \n",
      " 4   amount      8757 non-null   int64         \n",
      " 5   frequency   8757 non-null   int64         \n",
      " 6   date        8757 non-null   datetime64[ns]\n",
      " 7   year_month  8757 non-null   period[M]     \n",
      "dtypes: datetime64[ns](1), int64(4), object(2), period[M](1)\n",
      "memory usage: 547.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['time'],format='%Y/%m/%d')\n",
    "#df['year-month-first'] = df['date'].values.astype('datetime64[M]')\n",
    "df['year_month'] = df['date'].dt.to_period('M')\n",
    "print(df)\n",
    "print(df.describe())\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f625723e-9188-402f-9d53-15e05667a8ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            author  rating  time  year  amount  frequency  year_month\n",
      "date                                                                 \n",
      "2016-08-02       1       1     1     1       1          1           1\n",
      "2016-08-03       1       1     1     1       1          1           1\n",
      "2016-08-05       2       2     2     2       2          2           2\n",
      "2016-08-06       1       1     1     1       1          1           1\n",
      "2016-08-07       1       1     1     1       1          1           1\n",
      "...            ...     ...   ...   ...     ...        ...         ...\n",
      "2019-02-24      21      21    21    21      21         21          21\n",
      "2019-02-25      20      20    20    20      20         20          20\n",
      "2019-02-26      17      17    17    17      17         17          17\n",
      "2019-02-27      15      15    15    15      15         15          15\n",
      "2019-02-28      15      15    15    15      15         15          15\n",
      "\n",
      "[895 rows x 7 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#每日销量分析\n",
    "gp_date = df.groupby(by='date').count()\n",
    "print(gp_date)\n",
    "plt.figure(figsize=(20,4))\n",
    "gp_date['frequency'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "404db2a9-55bd-4ff0-9771-20a1e0b0faa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            author  rating  time  year  amount  frequency  date\n",
      "year_month                                                     \n",
      "2016-08         15      15    15    15      15         15    15\n",
      "2016-09         29      29    29    29      29         29    29\n",
      "2016-10         61      61    61    61      61         61    61\n",
      "2016-11         64      64    64    64      64         64    64\n",
      "2016-12        108     108   108   108     108        108   108\n",
      "2017-01         84      84    84    84      84         84    84\n",
      "2017-02         90      90    90    90      90         90    90\n",
      "2017-03        116     116   116   116     116        116   116\n",
      "2017-04        150     150   150   150     150        150   150\n",
      "2017-05        165     165   165   165     165        165   165\n",
      "2017-06        174     174   174   174     174        174   174\n",
      "2017-07        205     205   205   205     205        205   205\n",
      "2017-08        234     234   234   234     234        234   234\n",
      "2017-09        242     242   242   242     242        242   242\n",
      "2017-10        325     325   325   325     325        325   325\n",
      "2017-11        365     365   365   365     365        365   365\n",
      "2017-12        384     384   384   384     384        384   384\n",
      "2018-01        411     411   411   411     411        411   411\n",
      "2018-02        256     256   256   256     256        256   256\n",
      "2018-03        311     311   311   311     311        311   311\n",
      "2018-04        149     149   149   149     149        149   149\n",
      "2018-05        319     319   319   319     319        319   319\n",
      "2018-06        434     434   434   434     434        434   434\n",
      "2018-07        481     481   481   481     481        481   481\n",
      "2018-08        487     487   487   487     487        487   487\n",
      "2018-09        402     402   402   402     402        402   402\n",
      "2018-10        547     547   547   547     547        547   547\n",
      "2018-11        557     557   557   557     557        557   557\n",
      "2018-12        602     602   602   602     602        602   602\n",
      "2019-01        580     580   580   580     580        580   580\n",
      "2019-02        410     410   410   410     410        410   410\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#每月销量分析\n",
    "gp_month = df.groupby(by='year_month').count()\n",
    "print(gp_month)\n",
    "plt.figure(figsize=(20,4))\n",
    "gp_month['frequency'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c8d754e-0274-4063-85fa-c6318da2fc48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7662</th>\n",
       "      <td>yueah cin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>yuen ha</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>yuen ping michelle</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7665</th>\n",
       "      <td>yuh-ming</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7666</th>\n",
       "      <td>yuhao</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7667</th>\n",
       "      <td>yuhuan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7668</th>\n",
       "      <td>yuk wa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7669</th>\n",
       "      <td>yuk yin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7670</th>\n",
       "      <td>yuki</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7671</th>\n",
       "      <td>yukshan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7672</th>\n",
       "      <td>yun chieh</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7673</th>\n",
       "      <td>yun han</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>yunching</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>yung min</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7676</th>\n",
       "      <td>yungi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7677</th>\n",
       "      <td>yungyi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7678</th>\n",
       "      <td>yunjeong</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7679</th>\n",
       "      <td>yunjin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7680</th>\n",
       "      <td>yunmi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7681</th>\n",
       "      <td>yunsuk</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>yunus</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7683</th>\n",
       "      <td>yvonne</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7684</th>\n",
       "      <td>zazah jane</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7685</th>\n",
       "      <td>zheng qiang</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7686</th>\n",
       "      <td>zheng wei</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7687</th>\n",
       "      <td>zhi hao</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7688</th>\n",
       "      <td>zhuang wei</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7689</th>\n",
       "      <td>ziv</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7690</th>\n",
       "      <td>ziyi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7691</th>\n",
       "      <td>zona</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7692</th>\n",
       "      <td>zubi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7693</th>\n",
       "      <td>中鴻</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7694</th>\n",
       "      <td>久茵</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7695</th>\n",
       "      <td>仰立</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7696</th>\n",
       "      <td>佳鈴</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7697</th>\n",
       "      <td>千瑞</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7698</th>\n",
       "      <td>受恩</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7699</th>\n",
       "      <td>嘉文</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7700</th>\n",
       "      <td>國俊</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7701</th>\n",
       "      <td>國隆</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7702</th>\n",
       "      <td>士齊</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7703</th>\n",
       "      <td>孟璇</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7704</th>\n",
       "      <td>宛庭</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7705</th>\n",
       "      <td>宜暹</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7706</th>\n",
       "      <td>建翔</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7707</th>\n",
       "      <td>志坤</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7708</th>\n",
       "      <td>怡婷</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7709</th>\n",
       "      <td>懿馨</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7710</th>\n",
       "      <td>承澎</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7711</th>\n",
       "      <td>振裕</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7712</th>\n",
       "      <td>曉君</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7713</th>\n",
       "      <td>洪</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7714</th>\n",
       "      <td>淑楨</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7715</th>\n",
       "      <td>素鳳</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7716</th>\n",
       "      <td>翔</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7717</th>\n",
       "      <td>芊羽</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7718</th>\n",
       "      <td>華山</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7719</th>\n",
       "      <td>蘇</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7720</th>\n",
       "      <td>郁君</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7721</th>\n",
       "      <td>青慧</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  author  frequency\n",
       "7662           yueah cin          1\n",
       "7663             yuen ha          1\n",
       "7664  yuen ping michelle          2\n",
       "7665            yuh-ming          1\n",
       "7666               yuhao          1\n",
       "7667              yuhuan          1\n",
       "7668              yuk wa          1\n",
       "7669             yuk yin          1\n",
       "7670                yuki          1\n",
       "7671             yukshan          1\n",
       "7672           yun chieh          1\n",
       "7673             yun han          1\n",
       "7674            yunching          1\n",
       "7675            yung min          1\n",
       "7676               yungi          1\n",
       "7677              yungyi          1\n",
       "7678            yunjeong          1\n",
       "7679              yunjin          1\n",
       "7680               yunmi          1\n",
       "7681              yunsuk          1\n",
       "7682               yunus          1\n",
       "7683              yvonne          1\n",
       "7684          zazah jane          1\n",
       "7685         zheng qiang          1\n",
       "7686           zheng wei          1\n",
       "7687             zhi hao          1\n",
       "7688          zhuang wei          1\n",
       "7689                 ziv          1\n",
       "7690                ziyi          1\n",
       "7691                zona          1\n",
       "7692                zubi          1\n",
       "7693                  中鴻          1\n",
       "7694                  久茵          1\n",
       "7695                  仰立          1\n",
       "7696                  佳鈴          1\n",
       "7697                  千瑞          1\n",
       "7698                  受恩          1\n",
       "7699                  嘉文          1\n",
       "7700                  國俊          1\n",
       "7701                  國隆          1\n",
       "7702                  士齊          1\n",
       "7703                  孟璇          1\n",
       "7704                  宛庭          1\n",
       "7705                  宜暹          1\n",
       "7706                  建翔          1\n",
       "7707                  志坤          1\n",
       "7708                  怡婷          1\n",
       "7709                  懿馨          1\n",
       "7710                  承澎          1\n",
       "7711                  振裕          1\n",
       "7712                  曉君          1\n",
       "7713                   洪          1\n",
       "7714                  淑楨          1\n",
       "7715                  素鳳          1\n",
       "7716                   翔          1\n",
       "7717                  芊羽          1\n",
       "7718                  華山          1\n",
       "7719                   蘇          1\n",
       "7720                  郁君          1\n",
       "7721                  青慧          1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_user = df.groupby(by=\"author\")\n",
    "\n",
    "gp_user_c = gp_user['frequency'].count().reset_index()\n",
    "gp_user_c.tail(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe5291b4-c039-478a-8fa2-180db9a695dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7662</th>\n",
       "      <td>yueah cin</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>yuen ha</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>yuen ping michelle</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7665</th>\n",
       "      <td>yuh-ming</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7666</th>\n",
       "      <td>yuhao</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7667</th>\n",
       "      <td>yuhuan</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7668</th>\n",
       "      <td>yuk wa</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7669</th>\n",
       "      <td>yuk yin</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7670</th>\n",
       "      <td>yuki</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7671</th>\n",
       "      <td>yukshan</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7672</th>\n",
       "      <td>yun chieh</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7673</th>\n",
       "      <td>yun han</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>yunching</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>yung min</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7676</th>\n",
       "      <td>yungi</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7677</th>\n",
       "      <td>yungyi</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7678</th>\n",
       "      <td>yunjeong</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7679</th>\n",
       "      <td>yunjin</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7680</th>\n",
       "      <td>yunmi</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7681</th>\n",
       "      <td>yunsuk</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>yunus</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7683</th>\n",
       "      <td>yvonne</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7684</th>\n",
       "      <td>zazah jane</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7685</th>\n",
       "      <td>zheng qiang</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7686</th>\n",
       "      <td>zheng wei</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7687</th>\n",
       "      <td>zhi hao</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7688</th>\n",
       "      <td>zhuang wei</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7689</th>\n",
       "      <td>ziv</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7690</th>\n",
       "      <td>ziyi</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7691</th>\n",
       "      <td>zona</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7692</th>\n",
       "      <td>zubi</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7693</th>\n",
       "      <td>中鴻</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7694</th>\n",
       "      <td>久茵</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7695</th>\n",
       "      <td>仰立</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7696</th>\n",
       "      <td>佳鈴</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7697</th>\n",
       "      <td>千瑞</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7698</th>\n",
       "      <td>受恩</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7699</th>\n",
       "      <td>嘉文</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7700</th>\n",
       "      <td>國俊</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7701</th>\n",
       "      <td>國隆</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7702</th>\n",
       "      <td>士齊</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7703</th>\n",
       "      <td>孟璇</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7704</th>\n",
       "      <td>宛庭</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7705</th>\n",
       "      <td>宜暹</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7706</th>\n",
       "      <td>建翔</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7707</th>\n",
       "      <td>志坤</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7708</th>\n",
       "      <td>怡婷</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7709</th>\n",
       "      <td>懿馨</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7710</th>\n",
       "      <td>承澎</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7711</th>\n",
       "      <td>振裕</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7712</th>\n",
       "      <td>曉君</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7713</th>\n",
       "      <td>洪</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7714</th>\n",
       "      <td>淑楨</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7715</th>\n",
       "      <td>素鳳</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7716</th>\n",
       "      <td>翔</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7717</th>\n",
       "      <td>芊羽</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7718</th>\n",
       "      <td>華山</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7719</th>\n",
       "      <td>蘇</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7720</th>\n",
       "      <td>郁君</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7721</th>\n",
       "      <td>青慧</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  author  amount\n",
       "7662           yueah cin     110\n",
       "7663             yuen ha     110\n",
       "7664  yuen ping michelle     220\n",
       "7665            yuh-ming     110\n",
       "7666               yuhao     110\n",
       "7667              yuhuan     110\n",
       "7668              yuk wa     110\n",
       "7669             yuk yin     110\n",
       "7670                yuki     110\n",
       "7671             yukshan     110\n",
       "7672           yun chieh     110\n",
       "7673             yun han     110\n",
       "7674            yunching     110\n",
       "7675            yung min     110\n",
       "7676               yungi     110\n",
       "7677              yungyi     110\n",
       "7678            yunjeong     110\n",
       "7679              yunjin     110\n",
       "7680               yunmi     110\n",
       "7681              yunsuk     110\n",
       "7682               yunus     110\n",
       "7683              yvonne     110\n",
       "7684          zazah jane     110\n",
       "7685         zheng qiang     110\n",
       "7686           zheng wei     110\n",
       "7687             zhi hao     110\n",
       "7688          zhuang wei     110\n",
       "7689                 ziv     110\n",
       "7690                ziyi     110\n",
       "7691                zona     110\n",
       "7692                zubi     110\n",
       "7693                  中鴻     110\n",
       "7694                  久茵     110\n",
       "7695                  仰立     110\n",
       "7696                  佳鈴     110\n",
       "7697                  千瑞     110\n",
       "7698                  受恩     110\n",
       "7699                  嘉文     110\n",
       "7700                  國俊     110\n",
       "7701                  國隆     110\n",
       "7702                  士齊     110\n",
       "7703                  孟璇     110\n",
       "7704                  宛庭     110\n",
       "7705                  宜暹     110\n",
       "7706                  建翔     110\n",
       "7707                  志坤     110\n",
       "7708                  怡婷     110\n",
       "7709                  懿馨     110\n",
       "7710                  承澎     110\n",
       "7711                  振裕     110\n",
       "7712                  曉君     110\n",
       "7713                   洪     110\n",
       "7714                  淑楨     110\n",
       "7715                  素鳳     110\n",
       "7716                   翔     110\n",
       "7717                  芊羽     110\n",
       "7718                  華山     110\n",
       "7719                   蘇     110\n",
       "7720                  郁君     110\n",
       "7721                  青慧     110"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_user_a = gp_user['amount'].sum().reset_index()\n",
    "gp_user_a.tail(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4bde21e8-c1d0-409e-b27d-2e6d02ab84dd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7662</th>\n",
       "      <td>yueah cin</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>yuen ha</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>yuen ping michelle</td>\n",
       "      <td>2</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7665</th>\n",
       "      <td>yuh-ming</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7666</th>\n",
       "      <td>yuhao</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7667</th>\n",
       "      <td>yuhuan</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7668</th>\n",
       "      <td>yuk wa</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7669</th>\n",
       "      <td>yuk yin</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7670</th>\n",
       "      <td>yuki</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7671</th>\n",
       "      <td>yukshan</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7672</th>\n",
       "      <td>yun chieh</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7673</th>\n",
       "      <td>yun han</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>yunching</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>yung min</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7676</th>\n",
       "      <td>yungi</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7677</th>\n",
       "      <td>yungyi</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7678</th>\n",
       "      <td>yunjeong</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7679</th>\n",
       "      <td>yunjin</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7680</th>\n",
       "      <td>yunmi</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7681</th>\n",
       "      <td>yunsuk</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>yunus</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7683</th>\n",
       "      <td>yvonne</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7684</th>\n",
       "      <td>zazah jane</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7685</th>\n",
       "      <td>zheng qiang</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7686</th>\n",
       "      <td>zheng wei</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7687</th>\n",
       "      <td>zhi hao</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7688</th>\n",
       "      <td>zhuang wei</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7689</th>\n",
       "      <td>ziv</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7690</th>\n",
       "      <td>ziyi</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7691</th>\n",
       "      <td>zona</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7692</th>\n",
       "      <td>zubi</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7693</th>\n",
       "      <td>中鴻</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7694</th>\n",
       "      <td>久茵</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7695</th>\n",
       "      <td>仰立</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7696</th>\n",
       "      <td>佳鈴</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7697</th>\n",
       "      <td>千瑞</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7698</th>\n",
       "      <td>受恩</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7699</th>\n",
       "      <td>嘉文</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7700</th>\n",
       "      <td>國俊</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7701</th>\n",
       "      <td>國隆</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7702</th>\n",
       "      <td>士齊</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7703</th>\n",
       "      <td>孟璇</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7704</th>\n",
       "      <td>宛庭</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7705</th>\n",
       "      <td>宜暹</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7706</th>\n",
       "      <td>建翔</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7707</th>\n",
       "      <td>志坤</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7708</th>\n",
       "      <td>怡婷</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7709</th>\n",
       "      <td>懿馨</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7710</th>\n",
       "      <td>承澎</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7711</th>\n",
       "      <td>振裕</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7712</th>\n",
       "      <td>曉君</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7713</th>\n",
       "      <td>洪</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7714</th>\n",
       "      <td>淑楨</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7715</th>\n",
       "      <td>素鳳</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7716</th>\n",
       "      <td>翔</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7717</th>\n",
       "      <td>芊羽</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7718</th>\n",
       "      <td>華山</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7719</th>\n",
       "      <td>蘇</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7720</th>\n",
       "      <td>郁君</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7721</th>\n",
       "      <td>青慧</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  author  frequency  amount\n",
       "7662           yueah cin          1     110\n",
       "7663             yuen ha          1     110\n",
       "7664  yuen ping michelle          2     220\n",
       "7665            yuh-ming          1     110\n",
       "7666               yuhao          1     110\n",
       "7667              yuhuan          1     110\n",
       "7668              yuk wa          1     110\n",
       "7669             yuk yin          1     110\n",
       "7670                yuki          1     110\n",
       "7671             yukshan          1     110\n",
       "7672           yun chieh          1     110\n",
       "7673             yun han          1     110\n",
       "7674            yunching          1     110\n",
       "7675            yung min          1     110\n",
       "7676               yungi          1     110\n",
       "7677              yungyi          1     110\n",
       "7678            yunjeong          1     110\n",
       "7679              yunjin          1     110\n",
       "7680               yunmi          1     110\n",
       "7681              yunsuk          1     110\n",
       "7682               yunus          1     110\n",
       "7683              yvonne          1     110\n",
       "7684          zazah jane          1     110\n",
       "7685         zheng qiang          1     110\n",
       "7686           zheng wei          1     110\n",
       "7687             zhi hao          1     110\n",
       "7688          zhuang wei          1     110\n",
       "7689                 ziv          1     110\n",
       "7690                ziyi          1     110\n",
       "7691                zona          1     110\n",
       "7692                zubi          1     110\n",
       "7693                  中鴻          1     110\n",
       "7694                  久茵          1     110\n",
       "7695                  仰立          1     110\n",
       "7696                  佳鈴          1     110\n",
       "7697                  千瑞          1     110\n",
       "7698                  受恩          1     110\n",
       "7699                  嘉文          1     110\n",
       "7700                  國俊          1     110\n",
       "7701                  國隆          1     110\n",
       "7702                  士齊          1     110\n",
       "7703                  孟璇          1     110\n",
       "7704                  宛庭          1     110\n",
       "7705                  宜暹          1     110\n",
       "7706                  建翔          1     110\n",
       "7707                  志坤          1     110\n",
       "7708                  怡婷          1     110\n",
       "7709                  懿馨          1     110\n",
       "7710                  承澎          1     110\n",
       "7711                  振裕          1     110\n",
       "7712                  曉君          1     110\n",
       "7713                   洪          1     110\n",
       "7714                  淑楨          1     110\n",
       "7715                  素鳳          1     110\n",
       "7716                   翔          1     110\n",
       "7717                  芊羽          1     110\n",
       "7718                  華山          1     110\n",
       "7719                   蘇          1     110\n",
       "7720                  郁君          1     110\n",
       "7721                  青慧          1     110"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_user_m = pd.merge(left=gp_user_c,right=gp_user_a,on=\"author\",how=\"inner\")\n",
    "gp_user_m.tail(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "740b28bb-6261-4011-908d-37a98f62c867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2laCgIOnevbscOHDA05aSkiIBAQGepX379p62vXv3SmJiojRt2lSmT58uLperzl8PAABwpkBfBZ2hQ4eWCDpHjhyRCRMmyLx58+TEiRPSoUMHmThxoqd9586dkp6eLnl5eWbJzMw024uLi2XYsGHSo0cP85j9+/dLWlqaL14WAABwIJ+EndGjR8uYMWNKbNNeHA06v/jFL6R58+YyZcoUT6C5fPmy7Nu3T/r16ydNmjQxS+PGjU3b2rVrJT8/XxYuXCjt2rWTOXPmyNKlS8v93RqOCgoKSiwAAMBePgk7ixcvNsNS3rSnZ9KkSZ71Q4cOSUJCgvn/rKwsuXr1qhnaatiwoQwaNEiOHz9u2nbv3i29e/eW8PBws961a1fTu1OeuXPnSlRUlGeJi4urpVcJAADqbdjRWp2KXLx4URYsWCCTJ0826xpeOnbsKMuXL5c9e/aYmh53MNKeGe/n03qeBg0amKGussyYMcP0BLmXnJycGn1tAADAWYLEgWbPni0RERGemp2xY8eaxS01NdUEHA06GnxCQ0NL7B8WFiZFRUWmYLk0fWzpxwMAAHs5Luxs2rRJXnzxRdmxY4cEBweX+ZhmzZqZYa1Tp05JdHS0mY3lrbCwUEJCQuroiAGgeo7mnpPPvy6SNjEREh8b4evDAazlqLCTnZ0tycnJJux07tzZs12nk992222eouaMjAwJDAw09TY65VxrgLyfQ4uQNQQBgBN9U3RRUlZ9Kv/4LNezrV/CTfJC8m0SFV72P/IAWHBRwfPnz5si5fvuu09GjBgh586dM4teM6dbt27y9NNPy4cffijr1683tTzjx483Rck6Q0uHs5YtW2aeR2djJSUlmbodAHAiDTrbD58psU3XH1n13QxUAJb27GiI0UJkXUr31IwbN85MPR85cqQJMbquoUZpzc6SJUtMj5D2AGmPz5YtW3z4SgCg4qEr7x4dtysul9mefeZbhrSAGhbgsuRyw6dPn5Zdu3aZaegxMTGV3k97hXQKus7MioyMrNVjBIDNh76SCcv+VW77sgmJ0r9jszo9JsAfVeX72zE9Ozfq5ptvliFDhvj6MACgQq2jv7smWHm0WBmApTU7AFAftL2pkSlGbhAQUGK7rut2hrCAmkfYAYA6prOu+rSPLbFN13U7gJpnzTAWAPgLnV7++q97mmLkY2e/5To7QC0j7ACAj2jAIeQAtY9hLAAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAq9VY2NFbbF25cqWmng4AAMB3YWfq1KlSXFxcYtumTZukU6dONXNUAAAAvgw7r7zyyjVh50c/+pF8/vnnNXVcAAAAdX8F5ddff90zZLVy5UoJDw/3rGvPzh133FEzRwUAAOCLsLNs2TLzMyAgQFasWCFBQd/tHhgYKO3atZNVq1bV1HEBAADUiACXdstUkYabb775RiIjI8XfFRQUSFRUlOTn51vxegAAqA8KqvD9Xa2anYceekhCQ0Ore3wAAADOvuv5Sy+9JBcvXpScnBxTr+PtlltuqaljAwAA8E3YSU1Nld/+9rcm8HiHHa3l4Vo7AADASao1jDVz5kx59tln5cKFC3L16lXPQtABAABWhJ3GjRvLgAEDJDg4uOaPCAAAwNdh54UXXpBJkybJvn37avJYAAAAnFGzk5KSImfPnpWuXbtK06ZNS0z5Onr0aE0eHwAAQN2HnbS0tBv7rQAAAE4OO/Hx8TV/JAAAAE4JO23atDHTzN3TzvX/3ZiRBQAA/L5A2T3NXH9+++23smXLFrn77rtl9erVNX+EAAAAdX1vrLIUFRVJv379ZOfOneJPuDcWAAD+p9bvjVUWnZ315Zdf1tTTAQAA+LZA2btOR4ezTp06JY8++mjNHBUAAICTpp5r8GnZsqW0a9eupo4LAACgRlRrGOuuu+4yS8OGDSU3N9f8JOgAAABrws6JEyfkjjvukKSkJHNTUP2ZmJgoJ0+erPRznDlzxgyHHTt2zLNt79695nn0qszTp08vcUf1rVu3SqdOnSQ2NlYWLlxY4rneffddad26tbRo0UJWrVpVnZcEAAAsVa2w89BDD5mwo706Bw4ckK+++kpuv/12efDBBysddIYOHVoi6BQXF8uwYcOkR48eZkbX/v37PcNl+nuGDx8uycnJkpGRIStWrJDNmzd7AtLYsWNN6Fq3bp3MmjVLDh06VJ2XBQAALFStsLNt2zZ56qmnJDQ01KzrT13fvn17pfYfPXq0jBkzpsS2tWvXmulj2mujQ2Jz5syRpUuXmjYNN9pro4EmISHBBBp325IlS6R///4yceJE6dKli/zmN7+R5cuXV+dlAQAAC1Ur7GioeO2110ps0/Vbb721UvsvXrzY3EzU2+7du6V3794SHh5u1vUmo9q7427TQOOeAdazZ0/ZtWuXp23AgAGe5/FuK4v2IOncfO8FAADYq1qzsV566SUZOHCg6XHRuhu90/m5c+fMMFJ1762locN7uwabBg0aSF5enmnr3Lmzp00vHuSuDyq9n3dbWebOnSvPPPNMpV8rAACoh2FHe3D+/e9/y9///nfJycmRX/7yl6YGJyIiovoHEhTkGRZzCwsLM1dmLt3m3l7Wft5tZZkxY4ZMmzbNs65hKS4urtrHDQAALAw777//vgk6ly5dMutapOzu1fnrX/9arQOJjo42xcbeCgsLJSQkxLRpkXLp7e79ymsriwaj0qEKAADYq1o1O1pcfPnyZTPdu/RSXTrlXGdauWVnZ5v6Gg0zpdsyMzPNRQzL2s+7DQAAoFo9O//5n/9ppojrDKiKelGqQm8iqkNKy5YtkwkTJpjZWHr9Hq3b0WnnDz/8sGzcuNFczHD+/PmmZkiNHDlS+vTpY25VobU7zz//vIwbN65GjgkAANTTsKM9LjrF+5FHHvFs0wsAalHxlStXqncgQUFmGrleS0cvKBgYGChbtmwxbXohwUWLFsngwYOlUaNG0qRJE881eLp162aCjl73R+t1dGr61KlTq3UMAADAPgEu78sUV1KzZs3MVHPvGVJuNzKUpU6fPm2mjus09JiYmBJtOrR18OBB6du3rwk93nSaul7ZWXt+qtLbVJVbxAMAAGeoyvd3tcLOAw88YK5+rBcH1N4Ub+PHjxd/QtgBAMD/VOX7u1rDWNrDot58880S27/88ku/CzsAAMBu1ZqNlZqaaq50fPz4cRN8dNELCx4+fLjmjxAAAKCuw47OltJ6Hb2FgxYGa/hp2LChzJs370aOBQAAwBlhJysry8yYmjRpkrmC8k9/+lMzk8o9QwoAAMCvw07Hjh1NuNFp30eOHJGzZ8+aGVruWh4AAACnqFbYeeGFF+Qvf/mLfPPNN2ZmloafH//4x+bifwAAAE5Srannyr2bXkhw69at5p5UgwYNMhcH9CdMPQcAwP/U+tRzd8hx0wv5AQAAWDOMBQAA4C8IOwAAwGr+VWADwFH+8uFnsv3IGembcJNM7d/e14cDAGUi7ACoso8O58qYJZ941jOOfi3z1x2Stx7sLb3albyBLwD4GsNYAKrMO+h4G7V4R50fCwBcD2EHQJWHriqSupl75AFwFsIOgCrRGp2K/POz3Do7FgCoDMIOgCrp0y62wnYtVgYAJyHsAKiS39yTUGE7s7IAOA1hB0CV6ayrqmwHAF9i6jmAKtPp5cfmDTHFyFqjw3V2AFh5I1BbcCNQAADs/v5mGAsAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI3bRQAWmvTav2RXTp4kto6Wl/+/O3x9OADgU/TsABZ5IyNb2jyRLusPfCVnz12SD/Z9adbf/PhzXx8aAPgMYQewyNNr9pe5/Yn39tb5sQCAUxB2AIuGrioyefnOOjsWAHASR4WdtLQ0CQgIuGbR7cOHDy+xLSkpybPf1q1bpVOnThIbGysLFy706WsAfEVrdCryr8+/rrNjAQAncVTYGTNmjOTl5XmWnJwcE2D69u0rO3fulKysLE/bmjVrzD65ubkmCCUnJ0tGRoasWLFCNm/e7OuXAtS5HnFNK2zXYmUAqI8cFXZCQkKkSZMmnuX111+XESNGSFhYmLhcLrn11ls9bREREWYfDTctWrSQmTNnSkJCgsyaNUuWLl3q65cC1LlXf5lYYTuzsgDUV44KO94uXLggzz33nDz55JPyySefyJUrV6RVq1Ym5IwePdr07qjdu3dL//79zdCW6tmzp+zatavc5y0uLpaCgoISC2CLeSNurdJ2AKgPHBt2Vq5cKb169ZI2bdrIwYMHpVu3bpKeni47duyQ7OxsmTFjhnmchpX4+HjPfpGRkXLy5Mlyn3fu3LkSFRXlWeLi4urk9QB1YXSv1nJs3hAZ9KPmEtMo2PzUdd0OAPVVgEvHhxxIe2h+//vfy+DBg69p+8c//iH333+/nDlzRkaNGiV9+vSRlJQU06Y9QDrsdenSpXJ7dnRx07CkgSc/P98EJQAA4Hz6/a2dFpX5/nbkFZQPHz5slnvvvbfM9mbNmsnZs2dNaImOjjZFym6FhYWm9qc8oaGhZgEAAPWDI4ex3n77bRk6dKgEBwebde292bZtm6ddZ101b97chJbExESz7paZmSktW7b0yXEDAADncWTY+eCDD+Tuu+/2rHfp0kUee+wxE3hWr15t6nWmTJli2nTa+fbt22Xjxo1m6Gr+/PkycOBAHx49AABwEscNY50/f14+/vhjefXVVz3bHn/8cVOUPGjQIGncuLFMnTrVzNJSeh2eRYsWmdqeRo0amWnpehFCAAAARxcoV5WGIZ21pRcg1NBTGwVOAADAGfy+QLk6dPq59xR0AAAAx9bsAAAA1BTCDgAAsBphBwAAWI2wAwAArGZNgTLgdL3/uEG+LLwoP4gMlY+eTPL14QBAvUHPDlDLfvvm/5E2T6TL6cKLotd5OFlQbNYff/dTXx8aANQLhB2glv3/n54qc/tbO0/U+bEAQH1E2AFqeeiqIj+es7HOjgUA6ivCDlCLtEanIqcKiuvsWACgviLsALWoeeOQCtu1WBkAULsIO0At2vHUvRW2MysLAGofYQeoZaPuaFml7QCAmmXNXc+ri7ueo65oMbLW6HCdHQC4cfXyrueA0xFwAMA3GMYCAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKtxuwjUe22eSDc/A0Qke94QXx8OAKCGEXZQb/Wbt1GOf1PsWXf9v+DTLqahfDh9gE+PDQBQcxjGQr3lHXS8HTl7vs6PBQBQewg7qNdDV+WJv047AMB/EHaAMuiQFgDADoQdoAxarAwAsANhB/XSsevMumJWFgDYg7CDektnXVVlOwDAPzH1HPWWe3q5FiNrjQ7X2QEAOzmuZyclJUUCAgI8S/v27c32vXv3SmJiojRt2lSmT58uLtf3JaRbt26VTp06SWxsrCxcuNCHRw9/pAFHh7UIOgBgJ8eFnZ07d0p6errk5eWZJTMzU4qLi2XYsGHSo0cP075//35JS0szj8/NzZXhw4dLcnKyZGRkyIoVK2Tz5s2+fhkAAMAhHBV2Ll++LPv27ZN+/fpJkyZNzNK4cWNZu3at5Ofnm16bdu3ayZw5c2Tp0qVmHw03LVq0kJkzZ0pCQoLMmjXL0wYAAOCosJOVlSVXr16V7t27S8OGDWXQoEFy/Phx2b17t/Tu3VvCw8PN47p27Wp6d5S29e/f3wx5qZ49e8quXbvK/R3aS1RQUFBiAQAA9nJU2NEA07FjR1m+fLns2bNHgoKCZNKkSSaQxMfHex6nwaZBgwZmmKt0W2RkpJw8ebLc3zF37lyJioryLHFxcbX+ugAAgO84KuyMHTvW1OTceeedZkgqNTVVNmzYYHp7QkNDSzw2LCxMioqKTCDybnNvL8+MGTPMkJh7ycnJqdXXBAAAfMvRU8+bNWtmgs7NN99sZmN5KywslJCQEImOjjZFyqW3l0eDUengBAAA7OWonh2dUr5y5UrPus6uCgwMlC5dupj/d8vOzja1Nxp0dDq6d5vO3mrZsmWdHzsAAHAmR4Wdbt26ydNPPy0ffvihrF+/XiZPnizjx4+Xn/zkJ6Y2Z9myZeZxOhsrKSnJ1O3otPPt27fLxo0b5dKlSzJ//nwZOHCgr18KAABwCEcNY40bN85MPR85cqQJMrquwUbrcpYsWWKupaO9P9rbs2XLFrOPXkhw0aJFMnjwYGnUqJGZru6+Bg8AAECAy/tSxA53+vRpM61cp6HHxMSUaNOhrYMHD0rfvn1N6Kks7THSWVlarKwzuQAAgPNV5fvbr8JObSDsOFObJ9IrfYdyAED9U1CF729HDWMB3iGn9DZCDwDA7wuUAQAAahphB47u1alKOwAAZSHsAAAAqxF2AACA1Qg7cIzrFSBToAwAqA7CDgAAsBpTz+Eo7t4brrMDAKgphB04EgEHAFBTGMYCAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFbjCsqoEm7jAADwN4QdVDnklN5G6AEAOBnDWAAAwGqEHVSrV6cq7QAA+BJhBwAAWI2wAwAArEbYwXVdrwCZAmUAgJMRdgAAgNWYeo5KcffecJ0dAIC/IeygSgg4AAB/wzAWAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrOS7srFmzRtq2bStBQUHSvXt3OXDggNmekpIiAQEBnqV9+/aeffbu3SuJiYnStGlTmT59urhcLh++AgAA4CSOCjtHjhyRCRMmyLx58+TEiRPSoUMHmThxomnbuXOnpKenS15enlkyMzPN9uLiYhk2bJj06NHDPGb//v2Slpbm41cCAACcIsDloG6Q999/X06ePCmTJk0y65s3b5YhQ4ZIQUGBxMTEmADUqFGjEvusXr1aHnjgAfniiy8kPDxcdu/eLQ8//LBs27atUr9TnzsqKkry8/MlMjKyVl4XAACoWVX5/nbURQWHDh1aYv3QoUOSkJAgWVlZcvXqVTOspYHnrrvukldffVVuueUWE2569+5tgo7q2rWr6d0pj/YE6eL9ZgEAAHs5ahjL28WLF2XBggUyefJkE146duwoy5cvlz179ph6Hnfvj4aV+Ph4z35az9OgQQMz1FWWuXPnmiToXuLi4ursNQEAgLrnqJ4db7Nnz5aIiAhTsxMcHCxjx471tKWmppqAo0FHg09oaGiJfcPCwqSoqMgULJc2Y8YMmTZtmmddn8PfAw/3qwIAwM/CzqZNm+TFF1+UHTt2mKBTWrNmzcyw1qlTpyQ6OtrMxvJWWFgoISEhZT63BqPS4ciGkFN6G6EHAACHDmNlZ2dLcnKyCTudO3c223Q6+cqVKz2PycjIkMDAQNMjo1POdd17f63J0RAEAADgqLBz/vx5U6R83333yYgRI+TcuXNm0aLjp59+Wj788ENZv369qeMZP368KUru16+fGYpatmyZeY45c+ZIUlKSqduxWVm9OlVpBwCgvnDUMJYGGS1G1mXx4sUlemtGjRolI0eONCFm3LhxJtQordlZsmSJ6Q3SHiDt8dmyZYsPXwUAAHASR11n50acPn1adu3aZaah6zV5Kstfr7NTmZ4b6nYAALaqyve3NWGnuvw17Fwv8BB0AAA2K6jC97ejanYAAACsrtlB1bh7b7jODgAA5SPsWICAAwBA+RjGAgAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrcbuIWsQ9qwAA8D3CTi2HnNLbCD0AANQthrEAAIDVCDt10KtTlXYAAFCzCDsAAMBqhB0AAGA1wk4Nu14BMgXKAADULcIOAACwGlPPa4G794br7AAA4HuEnVpEwAEAwPcYxgIAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAq9X720W4XC7zs6CgwNeHAgAAKsn9ve3+Hq9IvQ87hYWF5mdcXJyvDwUAAFTjezwqKqrCxwS4KhOJLHb16lU5efKkNG7cWAICAqQ+JWINeDk5ORIZGSn1Ge/Fd3gfvsP78D3ei+/wPjjzfdD4okGnRYsWEhhYcVVOve/Z0TeoVatWUl/pB9YJH1on4L34Du/Dd3gfvsd78R3eB+e9D9fr0XGjQBkAAFiNsAMAAKxG2KmnQkNDZfbs2eZnfcd78R3eh+/wPnyP9+I7vA/+/z7U+wJlAABgN3p2AACA1Qg7AADAaoQdAABgNcKOpdasWSNt27aVoKAg6d69uxw4cOC6+wwfPtxcWNG9JCUl1cmxovalpaWV+LN1L7q9Il27di3x+IkTJ9bZMaN2nDlzRuLj4+XYsWM3dL5QnDPs/EykVfN84ehzhhYowy6HDx92NW3a1PXWW2+5Tp8+7fr5z3/u+vGPf3zd/X7wgx+4srKyXHl5eWY5d+6cywaPPPKIFuF7lnbt2l13ny1btrh++MMfumJiYlwLFixw+bvi4mLPn6suOTk5rtjYWPNZKc+3337rCg8Pd3311Vee/YqKilz+Kjc319WmTRtXdna2Z5t+3u+44w5XkyZNXL/73e9cV69erdRzvfPOO65bbrnF/J1ZuXKly5/eg169epm/B+73obrnC38/Z5T1eajOucLfzxe5ZXwmqnO+cPo5g7Bjob///e+uV155xbO+adMmV8OGDSvc54svvnDdfPPNLhvdeeedrvT0dM9fvoKCggofr39RIyMjXc8884zr3//+t+v2228376FN/vjHP7oefPDBCh+zbds2V+/evV02KOuEfuHCBfNl99BDD5mT+ODBg11//etfr/tc+uUeEhLiWrx4sWvPnj2u9u3buw4ePOjyB/fcc4/rueeeK/E+VOd84e/njLI+D9U5V9hwvrinjM9Edc4XTj9nEHbqgZdeesnVtWvXCh/zt7/9zXXTTTe5WrZsaZL5qFGjXF9//bXL3126dMmciAoLCyu9z6JFi8y/0tz/yl+9erVr7NixLlucP3/e1axZs3JPbG4LFy50tWrVyvyLLioqyjV58mQTEPxRWSf09957z/Ro6L9G1aeffurq06fPdZ/r0UcfdQ0cONCz/uc//9n11FNPufzB0aNHzc+Kvtgqc77w93NGWZ+H6pwrbDhfHL3OZ6Ky5wunnzOo2bHcxYsXZcGCBTJ58uQKH3fw4EHp1q2bpKeny44dOyQ7O1tmzJgh/i4rK8vc7FXrEBo2bCiDBg2S48ePV7jP7t27pX///p4bw/bs2VN27doltli5cqX06tVL2rRpU+HjDh06JP/xH/8h27Ztk3Xr1smGDRtk0aJF4o8WL14sKSkp1/w59+7dW8LDwz21Bvv377/uc+l+AwYM8Kz70+dD6zJq4nzh7+eMsj4P1TlX2HC+iL/OZ6Ky5wvHnzN8nbZQu5544glXt27dXBcvXqzSflu3bjXjz/7ujTfecPXo0cP10UcfmS7mIUOGlPhXeVnuv/9+1/z58z3rWoeg/+KzRWJioumqr6rXXnvNvJf+zPtfr9OmTXNNnTq1RLv+i/R6vRM6TPH222971vfu3VupnhAnKe9f8dU9X/jrOcP7fajOucKm84WU85mo7vnCaeeMen/Xc5tt2rRJXnzxRfOvruDg4Crt26xZMzl79qwUFxf75aXB3caOHWsWt9TUVPMvmYKCgnLv2qszUrxfc1hYmBQVFYkNDh8+bJZ77723yvvqZ+LEiRNii9J/zt5/1k2bNq30frZ8Pm7kfGHDOaM65wqbPw83er5w2jmDYSxLaZdycnKyOXl17tz5uo8fNWqU6Xp0y8jIkObNm/vlSet6f/m0q/rUqVPlPiY6Olpyc3M964WFhRISEiI2ePvtt2Xo0KGV+jK78847JScnp8RnonXr1mKL0n/Olf2ztvHzUdXzRX04Z1TmXGHr56E65wunnzMIOxY6f/68+YDed999MmLECDl37pxZtKdS/5Vy6dKla/bp0qWLPPbYY+bktXr1ajP2PmXKFPF306dPN2PO3n/5AgMDJS4urtx9EhMTzePcMjMzpWXLlmKDDz74QO6+++4S27755hu5cuXKNY/90Y9+JA899JB8/PHH8tprr5laDhs+E+X9OesXvvZK6JdXVfbz989HRecLVV/OGdU5V9j4ebje+cJvzxm+HkdDzdPZAN7XinAvOh7bunVrMwulNB2jf+CBB1wRERFmOqlOo9TZCf5u+fLlrvj4eNfGjRtd69atc3Xo0MH1q1/9yrTl5+eXWZug01LDwsJcGzZsMO2DBg1y/eY3v3H5O73ehU6ZPnDgQInt+tnIzMy85vE69fZnP/uZmYasn5vU1FSXvys9+0ZnE7mnm0+cONE1dOjQEq//8uXL1zyHztrSvyc67Vxn7nTv3t317LPPuvz1fajofKFsPmd4v86KzhX14XwhpWp2yjtf+Os5g7AD62nRpU6DjI6OdqWkpHgufFbeSdw9/TY4ONhMTdYToF5sDf6v9Al9zZo1Ztq0FtZq8Nm3b991T+jqySefNF8EWoiqBZhOuXAabuzzUN65QnG+8G8B+h9f9y4BTqTDGjq9tm/fvtKoUSNfHw5qyenTp81UYZ2GHhMTU+n9dJq6Fl/edddd1tRooPo4XzgbYQcAAFiNAmUAAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7ACoM3opfb1ar95pXC/H/69//cvXhwSgHuBGoADqTFpamrnX0JEjR8wl5693awYAqAmEHQB1Ru+KrfdU+sEPfmAWAKgLDGMBqHVvvvmmBAQEyDPPPGNuEKj//8Mf/tC0/epXv5Lf//738sYbb0jHjh3lpZde8uynw1y9evWSqKgouf/++yU/P9/Tpo/TGy62adNGHn30UfPT3XvkffPCY8eOmd/nfXNDDVxNmjSRiRMnmpt/Kj0GPZb//u//Nm3x8fGyfft2z36rVq2ShIQEcyyjR4/2HMuvf/1refjhhz2P27hxo7Ro0cLcMRuAMxB2ANS6kSNHSl5enjz++OOSnJxs/t+7XmfdunXyl7/8RZ599lkZPny42abDXD/96U/NsmfPHnP37d/+9rembefOneaO26+88oq8//778r//+7+VOo7Dhw+bu3trONLf/8knn8if/vQnT7s+z9GjR82dq/v06WPu5K0++ugjE4z0Ls6ffvqpGYqbPXu2afvFL35h7vrtvhj9e++9Jz//+c/NHbMBOAN/GwHUuuDgYNNbEhYWZu4jpf/fuHFjT7vW8GjgGTZsmOmtUenp6WY/DRWtW7eW3/3ud/I///M/pk1/Dho0yBQ733rrrTJ9+vRKHcdbb70l3bt3N8FFe2mmTp3qeU4VFBRkApT26owfP15ycnI8vUXjxo0zQUzbXn31VROa1D333GN6hzIyMkzgWbNmjYwaNapG3z8AN4aaHQA+98tf/tIMD3n74osvJDc3V5o2bWrWdVhIZ3NduHBBvvrqKzNU5KZhqDxFRUUlnlN7bTRsqcuXL5e4aaPeDDQ0NNT8v4Yyd2+Nhh7voTEdbtPFHZB0iO1vf/ub+X/t0bnzzjtv+D0BUHMIOwB8LiIi4pptrVq1kh49epjeGKXBQ+tktLfn5ptvNncqd9OhJzetz/Gul/F+nD6n9h7pcJS6cuVKiTAUGRlZ5vHFxcWZ2h+3zZs3y/z582Xt2rVmXXtyHnzwQROQdFjLu0YIgO8xjAXAkYYMGSLHjx83dTUNGzaUd9991wxdaejRIaQNGzaYIai9e/d6wovSYbD9+/ebGh/tGdJQ4qaFxf/85z/ls88+Mz04L7zwgkyYMOG6x6KPWb58uakPys7Olrlz58ott9ziadden2+//VZSU1MZwgIciLADwJF0qEnDjAaZtm3byjvvvGPWdajotttuk+eff16mTJkiSUlJZhjJbcCAAfKTn/zEzLjSwPTkk0962tq1ayevv/66TJs2Tdq3b28Kn3WW1fXosNTixYtNUfTtt99uepa8C5sbNGhgirD1ukGJiYm18G4AuBEBLvegNAD4qS1btphp495DTXXl/PnzpgfpD3/4g9x0003yxz/+sc6PAUDFqNkBgBvw9ddfS4cOHUzB8vr16319OADKQM8OAACwGjU7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDY7P8CySmHQNAUTBUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp_user_m.plot.scatter(x='frequency',y='amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52e28ae5-839e-416d-9299-5e45b0217ea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  author  frequency\n",
      "0                wenbiao          1\n",
      "1      Goh Yu Wen Eunice          1\n",
      "2               Hui Shan          1\n",
      "3                 Huihui          1\n",
      "4               KO-CHENG          1\n",
      "...                  ...        ...\n",
      "7717                  芊羽          1\n",
      "7718                  華山          1\n",
      "7719                   蘇          1\n",
      "7720                  郁君          1\n",
      "7721                  青慧          1\n",
      "\n",
      "[7722 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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06NBB6tevL+PGjROHw+F6zsaNG6Vly5bSoEEDSUtLK/V6K1askKZNm0pMTIwsXbrU69cDAAD8k0+CTlZWlrzxxhty8OBBc2zZskVKSkqkb9++kpCQYNq3b98uCxcuNI/fv3+/9OvXT5KTk2Xz5s2yePFiWb9+vSscDRo0SCZMmCCZmZkyceJE2blzpy8uCwAABHrQOXr0qHz66adyxRVXSL169cxRp04dycjIkIKCAtNb06JFC5k6daqkp6eb52iw0d4aDTPx8fEmzDjb5s+fL127dpWUlBRp1aqVjBgxQhYtWuTtywIAAH7I60Fn27ZtcuzYMWnbtq2cccYZ0rNnT/n6668lJydHEhMTJSIiwjyudevWpldHaZuGGR3mUh07dpTs7GxXW7du3Vyv797mifYcFRYWljoAAICdvB50NLycf/75ptdl69atEhISIkOHDjWBIy4uzvU4DTU1atQwQ1tl2yIjI2Xv3r3m6/LaPJk2bZrUrVvXdcTGxlbZtQIAgAALOlpPozU4l156qRmGmjt3rqxZs8b08oSFhZV6bHh4uBQXF5sw5N7mvF+V1+bJ+PHjzRCZ88jPz6+S6wQAAL4X4usTOOuss0zIadSokSksdldUVCShoaESFRVlCpLL3q/Ka/NEQ1HZQAUAAOzk9R4dnTa+ZMkS122dRRUcHGwKifVrp7y8PFNPo0FGp5y7t+ksrcaNG5uvy2sDAACBzetBp02bNvLQQw/JO++8I2+//bYMGzZMbrvtNrn66qtNvc2CBQvM43TWVffu3U2djk4t37Rpk6xdu1aOHDkiqamp0qNHD/O4AQMGyLJly0yR86FDh2T27NmuNgAAENi8PnR16623munlGlA0xOhtDTVaa6NTxXWtHO310V6eDRs2mOfoIoEzZ86UXr16Se3atc2UdOcaOxqcRo0aJe3btzf1OVr3M3z4cG9fFgAA8ENBDvflh/3Avn37zPRwnWoeHR1dqk2Hs3bs2CFJSUkm8JSdzbVnzx7p3LlzuTU6ZWkvkpl9dd9yCQ77fWr7ieye3vsUrwYAAFQF5+e3TizSGdfVJuh4G0EHAAB7gw6begIAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUqFHQuvfRSmT17tuzdu7fyzwgAAMCXQeeOO+6Qt99+W+Lj46Vz584yd+5c+f777yvrnAAAAHwXdFJSUuT111+X7777TkaMGCGbNm2SCy64QLp37y7p6enyyy+/VM7ZAQAA+KpGp6ioSPbv3y/ffvutCTdhYWGycuVK6d279+m8LAAAQKUIqciTZsyYIS+//LJ8/PHH0qVLF7n11lvN7fr165u6nbi4uMo5OwAAAG8HnYyMDLn99tvltddek+jo6FJttWrVkvfee+90zgkAAMB3QWft2rUnbKtbt6507NjxdM4JAADAdzU6v/32m5lenpWVZW7feeedkpqaau4HAACo1kHn3nvvlWeeeUZCQn7vENI6naVLl8pf//rXyj4/AAAA7wadl156SV555RVp27atuT1o0CATdF588cWKnwkAAIA/BJ2IiAgzrdzdDz/8YKaXAwAAVOugM3r0aLnhhhvkscceM707jz/+uNx0000yduzYU36tnj17ysKFC83XGzdulJYtW0qDBg0kLS2t1ONWrFghTZs2lZiYGNN75G7OnDnSsGFDad68uaxbt64ilwQAACxUoVlX9913nwkWGlCef/55iY2NNcHk5ptvPqXXWbx4sWRmZsrAgQNND1G/fv1MWEpOTjb3tWvXTrp27Sq5ublmeEwDzSWXXCLXX3+9XHzxxXL++eeb599///2ybNkyOfPMM82aPh999NFx094BAEDgCXI4HA5ffOMff/xRLrzwQqlXr548+OCD8tNPP5kC5+3bt0tQUJCsWrXK9BZp3Y8Gqx07dshbb71lnjtr1iwTjB599FG57rrrpFGjRvL000+7epsuuugis03FySgsLDRT4mPvWy7BYRHlPnb3dFZ8BgDAHzg/vwsKCiQyMrJye3Q+//xz+cc//iH5+flSNied7NCR9tz0799fDh8+bG7n5OSY3hsNOUrX4tEA5Gy75pprXM/VtilTprjabrnlllJt77777gmDTklJiTncf1AAAMBOFQo6AwYMMMNEOrwUGhp6ys9fv369vPPOO/Lpp5+aqerOwKE9PE6aznQ7CWeb+7YSJ9vmybRp02Ty5MmnfM4AACBAgk5eXp7Zvfycc8455efq5p933XWXPPXUU1KnTp3/O5GQkFKztsLDw6W4uPi02jwZP368jBkzxnVbg5LWGAEAAPtUaNbV4MGD5dlnn63QN3zkkUekQ4cOx+1wHhUVVWrKuu6M7uwtqmibJxqKtNfH/QAAAHaqUI+OhgstHNYZT507dy4VFiZOnFjuc5csWWKCiRYhK+19Wb58ufm6U6dOrsdt2bJFGjdubL7WYLR582a54447Tth25ZVXHtcGAAACW4WCjhYh9+rVy7VQoB7KWUhcHt3Z/OjRo67bOjU8MTHR7IauQ0i6YaiGJ907q0ePHq6aoMsuu0xGjRpl6nF0ny2dRq50PZ9hw4bJkCFDzDBWenq6mZUFAABQoaCzYMGCCn/DJk2alLpdu3Zts0CgHjNnzjQBSu/THh/nQoJt2rQxIad9+/amBic+Pl6GDx9u2vr27Wumoet9Snt2dJ0dAACACq+jo0NQq1evNr07Gkj+/ve/y9y5c01gOR1a6Kxr5iQlJZnA407X2NmzZ4/p8Slbh6OLBP7888+m7WR6lpxYRwcAgOqnStfR0VCjKyL/+c9/NrOvgoN/r2nW2VQvv/xyxc9axAxNuU8Xd6fTz92noLvTWh0AAIDTDjrz5s0z6+C0atXKrEhcs2ZNswWErkgMAABQraeXa/2MDlm50yEl3f8KAACgWvfoPPTQQ2b7Bi361e0UtIhY96ZixWEAAFDte3Ruu+02WbNmjVnZuEuXLqYI+IUXXjA1OwAAANW6R0ddccUV5gAAALAq6OisqBNN4f7yyy9P95wAAAB8F3ScC/k5t3DIysoye1/90fYPAAAAfh90dFE+d9dcc40kJyebvajuvPPOyjo3AAAA7xcje9KsWTMzxRwAAKBa9+joBpruNTrHjh2T7OxsOffccyvz3AAAALwfdLT3xp2GHt2bSoevAAAAqnXQmTRpUuWfCQAAgD8Ena5du/7hDuHr1q2r6DkBAAD4LujEx8fLq6++amZYNW3aVHbt2iXPPfecGbpKSEionDMDAADwRdDZtGmTvPbaa3LJJZe47rvuuuvMFhCzZ88+3XMCAADw3fTyAwcOmJlW7vR2YWFh5ZwVAACAr3p0hg0bJtdee63ceuutEhMTI/n5+bJkyRIZOXJkZZwTAACA74LO5MmTpXXr1rJ69Wr57LPPpFGjRpKeni79+vWrnLMCAADw5e7lAwYMMAcAAIBVNTpHjhyRqVOnSmJiojRu3Fg+/fRT6dixo5l9BQAAUK2DzvDhw2X58uVmK4iioiKJiIiQTp06yV133VX5ZwgAAODNoLNixQp5+eWXTbCpUaOGOR544AH54IMPKnoeAAAA/hF0YmNj5d1333Xd1lWSdfgqLi6uMs8NAADA+8XIqampZoHAZ599VoqLi2XMmDHy3nvvyQsvvHB6ZwMAAODroNOzZ0/Jzc01dTrt2rWTJk2ayGOPPSbNmzevzHMDAADwzfTyc889V/72t7+d3ncHAADwtxqdt956Sw4ePFj5ZwMAAODroKNbQGRlZVXmeQAAAPhH0NE9rZ588kk5evRo5Z8RAACAL2t0oqOjzdBVQkKC6d2pVauWq+22226rrHMDAADwftBZuHChhIWFmUNnXrmvp0PQAQAA1S7orFu3Trp06SLBwcGyfv36qj0rAAAAb9boXHXVVXLo0CHXbV0JmRodAABgRdBxOBylbiclJcnevXur4pwAAAC8G3S0/qa84AMAAFCtp5e7hx39umz4AQAAqJbFyNqDc/nll0uNGjXM7cLCQrnmmmskNDS01OM+/vjjyj9LAACAqgw6CxYskMr0008/yc6dO+W8886T+vXrV+prAwAAnFLQGTx4cKX9xF566SW58847JTY2Vr788kuzLs+NN95odkQfMmSIfPHFF5KSkiKpqamu4bGNGzeaxQn3799vNhMdM2aM6/VWrFghY8eOlSNHjsiMGTMkOTmZ3y4AAKjYFhCno6CgQIYPHy7vvvuubNu2TebMmSPjxo2TkpIS6du3r1ltWffR2r59uwlASsNNv379TIDZvHmzLF682LWWj4ajQYMGyYQJEyQzM1MmTpxoeooAAAC8HnS0tkf3yWrdurW5ffHFF8sPP/wgGRkZJgSlpaVJixYtZOrUqZKenm4eo8EmJibGhJn4+HgTZpxt8+fPl65du5oeoFatWsmIESNk0aJF3r4sAADgh7wedHS4SntglA41zZw5U/r37y85OTmSmJgoERERpk2DkPbqKG3TMOMcxurYsaNkZ2e72rp16+Z6ffc2T7TnSMOW+wEAAOzk9aDjpAGlUaNG8tZbb8ns2bNN4IiLi3O1a6jRGV66eWjZtsjISNdiheW1eTJt2jSpW7eu69DgBQAA7OSzoKM9Nm+//bYZitJhp5CQELNJqLvw8HApLi4+rs15vyqvzZPx48ebITLnkZ+fXyXXBwAAAjjoaI+NFh4///zzsnLlSomKijJFx+6KiorMOj1l25z3q/LaPNFQpL0+7gcAALCT14OOThPXWVZOGko09LRs2dLMqHLKy8sz9TQaZDp06FCqbcuWLdK4cWPzdXltAAAgsHk96OgCgc8++6w5dNhI18S5+uqrpVevXqbexrkwoc666t69u6nT0anlmzZtkrVr15oCZl1fp0ePHuZxAwYMkGXLlpmp6rq7utb7ONsAAEBg83rQOfvss80Cf7NmzZKLLrrI1NO88MILptZGp4rr9PAGDRrIqlWr5LHHHjPP0ds6O0vDUMOGDc06OQ899JBpa9OmjYwaNUrat29venI0GOk6PQAAAEEOP9uGfN++fWZ6uE41j46OLtWmw1k7duyQpKQkqV27dqk2nYq+Z88e6dy5c7k1OmVpL5KZfXXfcgkO+31q+4nsnt77FK8GAABUBefnt04sKq/e1u+CjrcRdAAAsDfo+GzWFQAAQFUj6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLZ8EnVWrVknz5s0lJCRE2rZtK5999pm5Pzc3Vzp06CD169eXcePGicPhcD1n48aN0rJlS2nQoIGkpaWVer0VK1ZI06ZNJSYmRpYuXer16wEAAP7J60Fn165dMmTIEJk+fbrs2bNHzjvvPElJSZGSkhLp27evJCQkSFZWlmzfvl0WLlxonrN//37p16+fJCcny+bNm2Xx4sWyfv16VzgaNGiQTJgwQTIzM2XixImyc+dOb18WAADwQ14POtp7oyHnpptukoYNG8rdd98tW7ZskYyMDCkoKDC9NS1atJCpU6dKenq6eY4GG+2t0TATHx9vwoyzbf78+dK1a1cTllq1aiUjRoyQRYsWefuyAACAH/J60OnTp48MHTrUdVt7XzS85OTkSGJiokRERJj7W7dubXp1lLZpmAkKCjK3O3bsKNnZ2a62bt26uV7Pvc0T7TkqLCwsdQAAADv5tBj5119/lRkzZsiwYcNM4IiLi3O1aaipUaOGHDx48Li2yMhI2bt3r/m6vDZPpk2bJnXr1nUdsbGxVXZ9AAAggIPOpEmTpFatWmbYSQuTw8LCSrWHh4dLcXHxcW3O+1V5bZ6MHz/eDJE5j/z8/Cq5NgAA4HshvvrG69atkzlz5sj7778vNWvWlKioKFNY7K6oqEhCQ0NNmxYkl71fldfmiYaisoEKAADYySc9Onl5eWYGlQadCy+80Nyn08p1RpX7Y7SeRoNM2TYtXm7cuLHH57m3AQCAwOb1oHP48GFTkHzttddK//795dChQ+ZISkoy9TYLFiwwj9NZV927dzd1Ojq1fNOmTbJ27Vo5cuSIpKamSo8ePczjBgwYIMuWLZNt27aZ15k9e7arDQAABDavD129/fbbZjaVHvPmzSvVg6NTxbWnRxcLDA4Olg0bNpg2XSRw5syZ0qtXL6ldu7bUq1fPtcZOmzZtZNSoUdK+fXtTn6MzuIYPH+7tywIAAH4oyOG+/LAf2Ldvn5kerlPNo6OjS7VpGNqxY4fp/dHA406Dky5A2Llz53JrdMrSXiQz++q+5RIc9vvU9hPZPb33KV4NAACoCs7Pb51YpDOuq03Q8TaCDgAA9gYdNvUEAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsJbPgs6BAwckLi5Odu/e7bovNzdXOnToIPXr15dx48aJw+FwtW3cuFFatmwpDRo0kLS0tFKvtWLFCmnatKnExMTI0qVLvXodAADAfwX7KuT06dOnVMgpKSmRvn37SkJCgmRlZcn27dtl4cKFpm3//v3Sr18/SU5Ols2bN8vixYtl/fr1rnA0aNAgmTBhgmRmZsrEiRNl586dvrgsAADgZ3wSdAYOHCi33HJLqfsyMjKkoKDA9Na0aNFCpk6dKunp6aZNg4321miYiY+PN2HG2TZ//nzp2rWrpKSkSKtWrWTEiBGyaNEiX1wWAADwMz4JOvPmzZORI0eWui8nJ0cSExMlIiLC3G7durXp1XG2aZgJCgoytzt27CjZ2dmutm7durlex73NE+05KiwsLHUAAAA7+SToaG1OWRo43O/XUFOjRg05ePDgcW2RkZGyd+9ej89zb/Nk2rRpUrduXdcRGxtbiVcGAAD8id/MugoJCZGwsLBS94WHh0txcfFxbc77PT3Pvc2T8ePHmyEy55Gfn18l1wMAAHwvRPxEVFSUKSx2V1RUJKGhoaZNC5LL3u983onaPNFQVDZQAQAAO/lNj45OK9cZVU55eXmmnkaDTNm2LVu2SOPGjT0+z70NAAAENr8JOldccYWpt1mwYIG5rbOuunfvbup0dGr5pk2bZO3atXLkyBFJTU2VHj16mMcNGDBAli1bJtu2bZNDhw7J7NmzXW0AACCw+c3Qldba6FRxXStHFwsMDg6WDRs2mDZdJHDmzJnSq1cvqV27ttSrV8+1xk6bNm1k1KhR0r59e1Ofo9PPhw8f7uOrAQAA/iDI4b78sB/Yt2+fmR6uU82jo6NLtelw1o4dOyQpKckEHnc6FX3Pnj3SuXPncmt0ytJeJDP76r7lEhz2+9T2E9k9vfcpXg0AAKgKzs9vnVikM66rTdDxNoIOAAD2Bh2/qdEBAACobAQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArBXi6xOwVbMH3zjpx+6e3rtKzwUAgEBFjw4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtUJ8fQKoGs0efOOkHrd7eu8qPxcAAHzFmh6d3Nxc6dChg9SvX1/GjRsnDofD16cEAAB8zIqgU1JSIn379pWEhATJysqS7du3y8KFC319WgAAwMesGLrKyMiQgoICSUtLk4iICJk6darcc889MmTIEF+fWkA62WEzxdAZAKAqWRF0cnJyJDEx0YQc1bp1a9Orc6LeHz2cNCCpYyXFf/h9CgsLT/qcTub1KvK6lf39ffm9q+r7/2lS5kk9Lndyj0r/3gAA73B+fvxRqUqQw4JilrFjx8ovv/wic+bMcd135plnyv/+9z9Ts+Pu4YcflsmTJ/vgLAEAQGXLz8+XJk2a2N2jExISImFhYaXuCw8Pl+Li4uOCzvjx42XMmDGu28eOHZMff/xRoqOjJSgoSLydRmNjY80vKTIyUgIF1811BwKum+sOBIU+vG7tpykqKpKYmJhyH2dF0ImKijKzrtzpxYeGhh73WA1EZUNRvXr1xJf0H0cg/Y/hxHUHFq47sHDdgSXSR9ddt27dwJh1pdPKN2/e7Lqdl5dn6nA0AAEAgMBlRdC54oorTPfZggULzG2dddW9e3epUaOGr08NAAD4kDU1OvPnz5fk5GSzWGBwcLBs2LBB/J0OoU2aNOm4oTTbcd1cdyDgurnuQBBWDa7billXTvv27ZPs7Gwz1VyLiwEAQGCzKugAAABYV6MDAADgCUEHAABYi6DjI6tWrZLmzZubQuq2bdvKZ599JoGmZ8+eAbf56gMPPGA2oA0UOklAFxPT7Vm6dOkiX375pa9PCZXswIEDEhcXJ7t37w6o9zdP1x0I728Hyrluf31/I+j4wK5du8yGo9OnT5c9e/bIeeedJykpKRJIFi9eLJmZJ7cnlS22bt0qc+fOlVmzZkmg/DufMmWK+dDbsWOHtGjRQm6//XaxlacPAF3IVNf50hXadUaobSWRes19+vQpdc2B8P7m6boD4f3tQDnX7c/vbwQdH9C/bvRN4KabbpKGDRvK3XffLVu2bJFAoVtu6P5k559/vgQK3Wpk6NChMnr0aPOXbiDQf9M6A/Liiy+Wc845R/7yl7/IF198ITby9AGgi5bqX7cJCQmSlZVlNhq27S/8gQMHyi233BJw72+erjsQ3t8GnuC6/f39jaDjA/qGqP8onHbu3Cnx8fESKPRNoH///uZDMFA8/fTTsm3bNmnWrJmsXr1afv31V7HdhRdeKOvWrZNPPvlECgoKzF97V111ldjI0wdARkaGue60tDTTm6ULmaanp4tN5s2bJyNHjgy49zdP1x0I72/zTnDd/v7+RtDxMf0HMWPGDBk2bJgEgvXr18s777wjqampEigOHTpkFtTSv3S++uormTlzplx++eVy+PBhsT3o3HDDDdKuXTuzn5xu0/LEE0+IjTx9AOTk5JgPO61PUq1btza9OjbRobpAfH870XXb/v4W5+G6q8P7G0HHx/QfSK1atawbw/bkl19+kbvuukueeuopqVOnjgSKlStXys8//2zeBCdPnixr1qwxm84uWrRIbPbhhx/Ka6+9Ju+//7789NNPZuXyXr16WVencqIPAN2Wxv3+oKAgsy3NwYMHJVDw/ma/ldXg/Y2g40ParT9nzhxZsmSJ1KxZU2z3yCOPmMLM3r17SyD55ptvzF/2DRo0MLd1Jor+dW9rvYrT0qVLzZDOJZdcYnYYfvTRR02hqvZ0BAL9PZddFj88PFyKi4slEPD+Fhi+qQbvb1bsdVUd6Q7r+heuvhFoF38g0De8/fv3m2EMpW/4y5cvN3/5a/2GrZo0aXJcN6528Xbq1ElspgWKWqTrpH/l6e/8t99+k0AQFRVlZl25059BaGio2I73N97fOvnR+xtBxwf0H4UW7F177bWmaE3HOJV28Wr3tq3ee+89OXr0qOv2/fffb/4SsHnKsdK/8O69915TsKe/d+3q1V6Nl156SWyWlJQkgwcPNrOudPaNrqnTqFEj89deINC/7rV2x/3DX2diaQCyGe9vv+P97SXxG7rXFbzr1Vdf1SKF4468vDxHIBk8eLBjwYIFjkDwn//8x5GYmOg444wzHM2bN3esXr3aYbtjx445pkyZ4jjnnHMcNWvWdLRr187x8ccfO2zm/v/xkSNHHGeeeabjueeeM7dTUlIcffr0cdh+3YH0/lbeddn8/iZlrtvf39/Y1BMAKon2WGjPjU6zVTrVVodwzjjjDAkODpYNGzYEzFAO4C8IOgBQhfbt2yfZ2dlmGCM6OtrXpwMEHIIOAACwFtPLAQCAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIDY6v8Bpyb5n29ubx8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用户购买数量分析\n",
    "gp_user = df.groupby(by='author')\n",
    "gp_user_f = gp_user['frequency'].count().reset_index()\n",
    "print(gp_user_f)\n",
    "gp_user_f.plot.hist(bins = 50)  #柱子宽度= （max-min）/bins\n",
    "\n",
    "plt.xlim(1,17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "68c09544-4717-41a2-9f7a-cc1736ca0004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ANGELINA</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>ARLENE</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Abigail</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Ace</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7615</th>\n",
       "      <td>yanyan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7626</th>\n",
       "      <td>yewon</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7646</th>\n",
       "      <td>yinung</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7660</th>\n",
       "      <td>yu</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>yuen ping michelle</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>603 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  author  frequency\n",
       "49              ANGELINA          3\n",
       "74                ARLENE          4\n",
       "83                 Aaron          3\n",
       "90               Abigail          6\n",
       "93                   Ace          2\n",
       "...                  ...        ...\n",
       "7615              yanyan          2\n",
       "7626               yewon          2\n",
       "7646              yinung          2\n",
       "7660                  yu          2\n",
       "7664  yuen ping michelle          2\n",
       "\n",
       "[603 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#购买两次及以上的情况\n",
    "twice_user = gp_user_f.reset_index(drop=True)\n",
    "twice_user = twice_user[twice_user['frequency'] > 1]\n",
    "twice_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7af12f95-c210-4c26-875b-1b374a417e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "twice_user['frequency'].plot.hist(bins = 50)\n",
    "plt.title('pucharse times more than 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8859a9c-9372-4a57-aee3-02de91302b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           author\n",
       "frequency        \n",
       "2             402\n",
       "3              99\n",
       "4              49\n",
       "5              25\n",
       "6              13\n",
       "7               7\n",
       "8               4\n",
       "11              1\n",
       "13              1\n",
       "15              1\n",
       "18              1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_user = twice_user.groupby(by='frequency').count()\n",
    "f_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b4d5d36d-5d03-4541-a90e-a07a345ba5d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>wenbiao</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Goh Yu Wen Eunice</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hui Shan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Huihui</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KO-CHENG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7689</th>\n",
       "      <td>芊羽</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7690</th>\n",
       "      <td>華山</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7691</th>\n",
       "      <td>蘇</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7692</th>\n",
       "      <td>郁君</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7693</th>\n",
       "      <td>青慧</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7694 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  author  frequency\n",
       "0                wenbiao          1\n",
       "1      Goh Yu Wen Eunice          1\n",
       "2               Hui Shan          1\n",
       "3                 Huihui          1\n",
       "4               KO-CHENG          1\n",
       "...                  ...        ...\n",
       "7689                  芊羽          1\n",
       "7690                  華山          1\n",
       "7691                   蘇          1\n",
       "7692                  郁君          1\n",
       "7693                  青慧          1\n",
       "\n",
       "[7694 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#购买用户在2-5次的用户分析\n",
    "#gp_user_f = gp_user_f[(gp_user_f['frequency']>1) & (gp_user_f['frequency']<6)].reset_index(drop=True) # 方案1\n",
    "gp_user_1_5 = gp_user_f[gp_user_f[\"frequency\"].between(1, 5, inclusive=\"both\")].reset_index(drop=True)\n",
    "gp_user_1_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e29da43e-f996-4322-be8f-3f8d162c2343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frequency\n",
       "1    7119\n",
       "2     402\n",
       "3      99\n",
       "4      49\n",
       "5      25\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_user_f_values = gp_user_1_5['frequency'].value_counts()\n",
    "gp_user_f_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b24bd2b-aa24-4d80-bf30-ef6d41366c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['1次','2次','3次','4次','5次']\n",
    "\n",
    "plt.pie(gp_user_f_values,labels=labels,autopct='%1.1f%%')\n",
    "plt.title('purchase times between 1 and 5')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3a0a023-136f-4255-af6e-41c4e47d1a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANGELINA</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARLENE</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ace</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agnes</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>yanyan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>yewon</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>yinung</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>yu</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>yuen ping michelle</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>575 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 author  frequency\n",
       "0              ANGELINA          3\n",
       "1                ARLENE          4\n",
       "2                 Aaron          3\n",
       "3                   Ace          2\n",
       "4                 Agnes          3\n",
       "..                  ...        ...\n",
       "570              yanyan          2\n",
       "571               yewon          2\n",
       "572              yinung          2\n",
       "573                  yu          2\n",
       "574  yuen ping michelle          2\n",
       "\n",
       "[575 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#购买用户在2-5次的用户分析\n",
    "gp_user_2_5 = gp_user_f[gp_user_f[\"frequency\"].between(2, 5, inclusive=\"both\")].reset_index(drop=True)\n",
    "gp_user_2_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78cac2e4-dc4c-4e37-bd60-9f07c344eb2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frequency\n",
       "2    402\n",
       "3     99\n",
       "4     49\n",
       "5     25\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_user_f_values = gp_user_2_5['frequency'].value_counts()\n",
    "gp_user_f_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67242412-0ba0-4fa1-84ac-7ebcb464c4aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['2次','3次','4次','5次']\n",
    "\n",
    "plt.pie(gp_user_f_values,labels=labels,autopct='%1.1f%%')\n",
    "plt.title('purchase times between 2 and 5')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "75e58824-11a0-4bb3-9581-f82c48ed33bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           author  rating       time  year  amount  frequency       date  \\\n",
      "0           YOUNG      60  2019/2/28  2019     110          1 2019-02-28   \n",
      "1       SHING YAN     100  2019/2/28  2019     110          1 2019-02-28   \n",
      "2             Sha      60  2019/2/28  2019     110          1 2019-02-28   \n",
      "3        Mary Mae     100  2019/2/28  2019     110          1 2019-02-28   \n",
      "4  Maria Cristina     100  2019/2/28  2019     110          1 2019-02-28   \n",
      "\n",
      "  year_month  \n",
      "0    2019-02  \n",
      "1    2019-02  \n",
      "2    2019-02  \n",
      "3    2019-02  \n",
      "4    2019-02  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>2016-08</th>\n",
       "      <th>2016-09</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>wenbiao</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goh Yu Wen Eunice</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hui Shan</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huihui</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO-CHENG</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芊羽</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>華山</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>蘇</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>郁君</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青慧</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7722 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month          2016-08  2016-09  2016-10  2016-11  2016-12  2017-01  \\\n",
       "author                                                                     \n",
       "      wenbiao           0.0      0.0      0.0      0.0      0.0      0.0   \n",
       " Goh Yu Wen Eunice      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       " Hui Shan               0.0      0.0      0.0      0.0      1.0      0.0   \n",
       " Huihui                 0.0      0.0      0.0      0.0      0.0      0.0   \n",
       " KO-CHENG               0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "...                     ...      ...      ...      ...      ...      ...   \n",
       "芊羽                      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "華山                      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "蘇                       0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "郁君                      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "青慧                      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "year_month          2017-02  2017-03  2017-04  2017-05  ...  2018-05  2018-06  \\\n",
       "author                                                  ...                     \n",
       "      wenbiao           0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       " Goh Yu Wen Eunice      0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       " Hui Shan               0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       " Huihui                 0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       " KO-CHENG               0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       "...                     ...      ...      ...      ...  ...      ...      ...   \n",
       "芊羽                      0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       "華山                      0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       "蘇                       0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       "郁君                      0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       "青慧                      0.0      0.0      0.0      0.0  ...      0.0      0.0   \n",
       "\n",
       "year_month          2018-07  2018-08  2018-09  2018-10  2018-11  2018-12  \\\n",
       "author                                                                     \n",
       "      wenbiao           0.0      0.0      0.0      0.0      0.0      1.0   \n",
       " Goh Yu Wen Eunice      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       " Hui Shan               0.0      0.0      0.0      0.0      0.0      0.0   \n",
       " Huihui                 0.0      0.0      0.0      0.0      0.0      0.0   \n",
       " KO-CHENG               1.0      0.0      0.0      0.0      0.0      0.0   \n",
       "...                     ...      ...      ...      ...      ...      ...   \n",
       "芊羽                      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "華山                      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "蘇                       0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "郁君                      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "青慧                      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "year_month          2019-01  2019-02  \n",
       "author                                \n",
       "      wenbiao           0.0      0.0  \n",
       " Goh Yu Wen Eunice      0.0      0.0  \n",
       " Hui Shan               0.0      0.0  \n",
       " Huihui                 0.0      0.0  \n",
       " KO-CHENG               0.0      0.0  \n",
       "...                     ...      ...  \n",
       "芊羽                      0.0      0.0  \n",
       "華山                      0.0      0.0  \n",
       "蘇                       0.0      0.0  \n",
       "郁君                      0.0      0.0  \n",
       "青慧                      0.0      0.0  \n",
       "\n",
       "[7722 rows x 31 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#复购率分析  在某一时间窗口内（多指一个月）消费此处在两次及以上用户在总消费用户的占比\n",
    "print(df.head())\n",
    "pivot_user = df.pivot_table(index=\"author\",values=\"frequency\",columns=\"year_month\",aggfunc=\"count\" ).fillna(0)\n",
    "pivot_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fb7a3060-5717-405f-a9bc-df753ba03b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>year_month</th>\n",
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       "      <th>2016-09</th>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO-CHENG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芊羽</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>華山</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>蘇</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>郁君</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青慧</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7722 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month          2016-08  2016-09  2016-10  2016-11  2016-12  2017-01  \\\n",
       "author                                                                     \n",
       "      wenbiao           NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " Goh Yu Wen Eunice      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " Hui Shan               NaN      NaN      NaN      NaN      0.0      NaN   \n",
       " Huihui                 NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " KO-CHENG               NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "...                     ...      ...      ...      ...      ...      ...   \n",
       "芊羽                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "華山                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "蘇                       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "郁君                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "青慧                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "year_month          2017-02  2017-03  2017-04  2017-05  ...  2018-05  2018-06  \\\n",
       "author                                                  ...                     \n",
       "      wenbiao           NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       " Goh Yu Wen Eunice      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       " Hui Shan               NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       " Huihui                 NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       " KO-CHENG               NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "...                     ...      ...      ...      ...  ...      ...      ...   \n",
       "芊羽                      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "華山                      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "蘇                       NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "郁君                      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "青慧                      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "\n",
       "year_month          2018-07  2018-08  2018-09  2018-10  2018-11  2018-12  \\\n",
       "author                                                                     \n",
       "      wenbiao           NaN      NaN      NaN      NaN      NaN      0.0   \n",
       " Goh Yu Wen Eunice      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " Hui Shan               NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " Huihui                 NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " KO-CHENG               0.0      NaN      NaN      NaN      NaN      NaN   \n",
       "...                     ...      ...      ...      ...      ...      ...   \n",
       "芊羽                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "華山                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "蘇                       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "郁君                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "青慧                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "year_month          2019-01  2019-02  \n",
       "author                                \n",
       "      wenbiao           NaN      NaN  \n",
       " Goh Yu Wen Eunice      NaN      NaN  \n",
       " Hui Shan               NaN      NaN  \n",
       " Huihui                 NaN      NaN  \n",
       " KO-CHENG               NaN      NaN  \n",
       "...                     ...      ...  \n",
       "芊羽                      NaN      NaN  \n",
       "華山                      NaN      NaN  \n",
       "蘇                       NaN      NaN  \n",
       "郁君                      NaN      NaN  \n",
       "青慧                      NaN      NaN  \n",
       "\n",
       "[7722 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_user_re =pivot_user.map(lambda x: 1 if x>1 else 0 if x == 1 else np.nan)\n",
    "pivot_user_re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "871321a8-9083-431f-9526-b2f7b3a4ce30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_month\n",
       "2016-08     0.0\n",
       "2016-09     2.0\n",
       "2016-10     0.0\n",
       "2016-11     0.0\n",
       "2016-12     1.0\n",
       "2017-01     0.0\n",
       "2017-02     1.0\n",
       "2017-03     0.0\n",
       "2017-04     3.0\n",
       "2017-05     3.0\n",
       "2017-06     2.0\n",
       "2017-07     1.0\n",
       "2017-08     1.0\n",
       "2017-09     4.0\n",
       "2017-10     4.0\n",
       "2017-11     4.0\n",
       "2017-12     3.0\n",
       "2018-01     6.0\n",
       "2018-02     2.0\n",
       "2018-03     2.0\n",
       "2018-04     1.0\n",
       "2018-05     5.0\n",
       "2018-06     5.0\n",
       "2018-07     5.0\n",
       "2018-08     7.0\n",
       "2018-09     9.0\n",
       "2018-10     4.0\n",
       "2018-11     7.0\n",
       "2018-12    12.0\n",
       "2019-01    10.0\n",
       "2019-02     4.0\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_user_re.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e990efd-df07-45f8-bc29-d73ea093a29d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pivot_user_re.sum()/pivot_user_re.count()).plot()\n",
    "plt.xlabel(\"时间\")\n",
    "plt.xlabel(\"复购率\")\n",
    "plt.title('用户复购率')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b6958dce-b430-4cf5-84bc-edefc086a568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot_user_re.sum().plot()\n",
    "plt.xlabel(\"时间\")\n",
    "plt.xlabel(\"复购人数\")\n",
    "plt.title('每月用户复购人数折线图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f36f5caa-d707-4578-a201-78b9babe5636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>2016-08</th>\n",
       "      <th>2016-09</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>wenbiao</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goh Yu Wen Eunice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hui Shan</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huihui</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO-CHENG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芊羽</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>華山</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>蘇</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>郁君</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青慧</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7722 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month          2016-08  2016-09  2016-10  2016-11  2016-12  2017-01  \\\n",
       "author                                                                     \n",
       "      wenbiao           NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " Goh Yu Wen Eunice      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " Hui Shan               NaN      NaN      NaN      NaN      0.0      NaN   \n",
       " Huihui                 NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " KO-CHENG               NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "...                     ...      ...      ...      ...      ...      ...   \n",
       "芊羽                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "華山                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "蘇                       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "郁君                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "青慧                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "year_month          2017-02  2017-03  2017-04  2017-05  ...  2018-05  2018-06  \\\n",
       "author                                                  ...                     \n",
       "      wenbiao           NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       " Goh Yu Wen Eunice      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       " Hui Shan               NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       " Huihui                 NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       " KO-CHENG               NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "...                     ...      ...      ...      ...  ...      ...      ...   \n",
       "芊羽                      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "華山                      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "蘇                       NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "郁君                      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "青慧                      NaN      NaN      NaN      NaN  ...      NaN      NaN   \n",
       "\n",
       "year_month          2018-07  2018-08  2018-09  2018-10  2018-11  2018-12  \\\n",
       "author                                                                     \n",
       "      wenbiao           NaN      NaN      NaN      NaN      NaN      0.0   \n",
       " Goh Yu Wen Eunice      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " Hui Shan               NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " Huihui                 NaN      NaN      NaN      NaN      NaN      NaN   \n",
       " KO-CHENG               0.0      NaN      NaN      NaN      NaN      NaN   \n",
       "...                     ...      ...      ...      ...      ...      ...   \n",
       "芊羽                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "華山                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "蘇                       NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "郁君                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "青慧                      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "year_month          2019-01  2019-02  \n",
       "author                                \n",
       "      wenbiao           NaN      NaN  \n",
       " Goh Yu Wen Eunice      NaN      NaN  \n",
       " Hui Shan               NaN      NaN  \n",
       " Huihui                 NaN      NaN  \n",
       " KO-CHENG               NaN      NaN  \n",
       "...                     ...      ...  \n",
       "芊羽                      NaN      NaN  \n",
       "華山                      NaN      NaN  \n",
       "蘇                       NaN      NaN  \n",
       "郁君                      NaN      NaN  \n",
       "青慧                      NaN      NaN  \n",
       "\n",
       "[7722 rows x 31 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#回购率分析\n",
    "\n",
    "def judge_back(data):\n",
    "    status = []\n",
    "    status.append(0 if data.iloc[0] >0 else np.nan)\n",
    "    for i in range(len(data) -1):\n",
    "        if data.iloc[i+1] >= 1:\n",
    "            if data.iloc[i] >= 1:\n",
    "                status.append(1)\n",
    "            else:\n",
    "                status.append(0)\n",
    "        else:\n",
    "            status.append(np.nan)\n",
    "    return pd.Series(status,pivot_user.columns)\n",
    "\n",
    "pivot_user_back = pivot_user.apply(lambda x:judge_back(x),axis=1)\n",
    "pivot_user_back\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "649be46c-6cfd-482e-ae16-f195e44f5c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_month\n",
       "2016-08     0.0\n",
       "2016-09     0.0\n",
       "2016-10     0.0\n",
       "2016-11     0.0\n",
       "2016-12     0.0\n",
       "2017-01     2.0\n",
       "2017-02     1.0\n",
       "2017-03     1.0\n",
       "2017-04     2.0\n",
       "2017-05     2.0\n",
       "2017-06     3.0\n",
       "2017-07     1.0\n",
       "2017-08     3.0\n",
       "2017-09     2.0\n",
       "2017-10     4.0\n",
       "2017-11     4.0\n",
       "2017-12     6.0\n",
       "2018-01     4.0\n",
       "2018-02     2.0\n",
       "2018-03     5.0\n",
       "2018-04     4.0\n",
       "2018-05     2.0\n",
       "2018-06    13.0\n",
       "2018-07    14.0\n",
       "2018-08    11.0\n",
       "2018-09    11.0\n",
       "2018-10    11.0\n",
       "2018-11    14.0\n",
       "2018-12    20.0\n",
       "2019-01    11.0\n",
       "2019-02     9.0\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_user_back.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "379a59a4-5455-4ba2-9648-4a00c6e22bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pivot_user_back.sum()/pivot_user_back.count()).plot()\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('back purchase rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d8a1ce86-841f-43ba-89c4-2f69a8d3d311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#回购人数分析\n",
    "\n",
    "(pivot_user_back.sum()).plot()\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('back purchase number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "16549c49-8a92-406d-a875-cbf0dcbeb920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year_month         2016-08 2016-09 2016-10 2016-11 2016-12   2017-01  \\\n",
      "author                                                                 \n",
      "      wenbiao        noreg   noreg   noreg   noreg   noreg     noreg   \n",
      " Goh Yu Wen Eunice   noreg   noreg   noreg   noreg   noreg     noreg   \n",
      " Hui Shan            noreg   noreg   noreg   noreg     new  inactive   \n",
      " Huihui              noreg   noreg   noreg   noreg   noreg     noreg   \n",
      " KO-CHENG            noreg   noreg   noreg   noreg   noreg     noreg   \n",
      "...                    ...     ...     ...     ...     ...       ...   \n",
      "芊羽                   noreg   noreg   noreg   noreg   noreg     noreg   \n",
      "華山                   noreg   noreg   noreg   noreg   noreg     noreg   \n",
      "蘇                    noreg   noreg   noreg   noreg   noreg     noreg   \n",
      "郁君                   noreg   noreg   noreg   noreg   noreg     noreg   \n",
      "青慧                   noreg   noreg   noreg   noreg   noreg     noreg   \n",
      "\n",
      "year_month           2017-02   2017-03   2017-04   2017-05  ...   2018-05  \\\n",
      "author                                                      ...             \n",
      "      wenbiao          noreg     noreg     noreg     noreg  ...     noreg   \n",
      " Goh Yu Wen Eunice     noreg     noreg     noreg     noreg  ...  inactive   \n",
      " Hui Shan           inactive  inactive  inactive  inactive  ...  inactive   \n",
      " Huihui                noreg     noreg     noreg     noreg  ...  inactive   \n",
      " KO-CHENG              noreg     noreg     noreg     noreg  ...     noreg   \n",
      "...                      ...       ...       ...       ...  ...       ...   \n",
      "芊羽                     noreg     noreg     noreg     noreg  ...  inactive   \n",
      "華山                     noreg     noreg     noreg     noreg  ...  inactive   \n",
      "蘇                      noreg     noreg     noreg     noreg  ...  inactive   \n",
      "郁君                     noreg     noreg     noreg     noreg  ...  inactive   \n",
      "青慧                     noreg     noreg     noreg     noreg  ...  inactive   \n",
      "\n",
      "year_month           2018-06   2018-07   2018-08   2018-09   2018-10  \\\n",
      "author                                                                 \n",
      "      wenbiao          noreg     noreg     noreg     noreg     noreg   \n",
      " Goh Yu Wen Eunice  inactive  inactive  inactive  inactive  inactive   \n",
      " Hui Shan           inactive  inactive  inactive  inactive  inactive   \n",
      " Huihui             inactive  inactive  inactive  inactive  inactive   \n",
      " KO-CHENG              noreg       new  inactive  inactive  inactive   \n",
      "...                      ...       ...       ...       ...       ...   \n",
      "芊羽                  inactive  inactive  inactive  inactive  inactive   \n",
      "華山                  inactive  inactive  inactive  inactive  inactive   \n",
      "蘇                   inactive  inactive  inactive  inactive  inactive   \n",
      "郁君                  inactive  inactive  inactive  inactive  inactive   \n",
      "青慧                  inactive  inactive  inactive  inactive  inactive   \n",
      "\n",
      "year_month           2018-11   2018-12   2019-01   2019-02  \n",
      "author                                                      \n",
      "      wenbiao          noreg       new  inactive  inactive  \n",
      " Goh Yu Wen Eunice  inactive  inactive  inactive  inactive  \n",
      " Hui Shan           inactive  inactive  inactive  inactive  \n",
      " Huihui             inactive  inactive  inactive  inactive  \n",
      " KO-CHENG           inactive  inactive  inactive  inactive  \n",
      "...                      ...       ...       ...       ...  \n",
      "芊羽                  inactive  inactive  inactive  inactive  \n",
      "華山                  inactive  inactive  inactive  inactive  \n",
      "蘇                   inactive  inactive  inactive  inactive  \n",
      "郁君                  inactive  inactive  inactive  inactive  \n",
      "青慧                  inactive  inactive  inactive  inactive  \n",
      "\n",
      "[7722 rows x 31 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>2016-08</th>\n",
       "      <th>2016-09</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inactive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>269</td>\n",
       "      <td>349</td>\n",
       "      <td>434</td>\n",
       "      <td>545</td>\n",
       "      <td>686</td>\n",
       "      <td>...</td>\n",
       "      <td>3599</td>\n",
       "      <td>3866</td>\n",
       "      <td>4228</td>\n",
       "      <td>4643</td>\n",
       "      <td>5059</td>\n",
       "      <td>5373</td>\n",
       "      <td>5827</td>\n",
       "      <td>6292</td>\n",
       "      <td>6801</td>\n",
       "      <td>7316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>15.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>81</td>\n",
       "      <td>85</td>\n",
       "      <td>112</td>\n",
       "      <td>142</td>\n",
       "      <td>156</td>\n",
       "      <td>...</td>\n",
       "      <td>266</td>\n",
       "      <td>382</td>\n",
       "      <td>407</td>\n",
       "      <td>420</td>\n",
       "      <td>330</td>\n",
       "      <td>464</td>\n",
       "      <td>461</td>\n",
       "      <td>503</td>\n",
       "      <td>490</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "      <td>48</td>\n",
       "      <td>52</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month  2016-08  2016-09  2016-10  2016-11  2016-12  2017-01  2017-02  \\\n",
       "active          NaN      NaN      NaN      NaN      NaN        2        1   \n",
       "inactive        NaN     15.0     42.0    102.0    165.0      269      349   \n",
       "new            15.0     27.0     61.0     63.0    106.0       81       85   \n",
       "return          NaN      NaN      NaN      1.0      1.0        1        3   \n",
       "\n",
       "year_month  2017-03  2017-04  2017-05  ...  2018-05  2018-06  2018-07  \\\n",
       "active            1        2        2  ...        2       13       14   \n",
       "inactive        434      545      686  ...     3599     3866     4228   \n",
       "new             112      142      156  ...      266      382      407   \n",
       "return            3        3        4  ...       46       34       53   \n",
       "\n",
       "year_month  2018-08  2018-09  2018-10  2018-11  2018-12  2019-01  2019-02  \n",
       "active           11       11       11       14       20       11        9  \n",
       "inactive       4643     5059     5373     5827     6292     6801     7316  \n",
       "new             420      330      464      461      503      490      352  \n",
       "return           48       52       68       75       65       68       45  \n",
       "\n",
       "[4 rows x 31 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户分层分析 \n",
    "def judge_type(data):\n",
    "    status = []\n",
    "    for i in range(len(data)):  \n",
    "        if i == 0:\n",
    "            if data.iloc[i] >= 1:\n",
    "                status.append('new')\n",
    "            else:\n",
    "                status.append('noreg')\n",
    "        else:\n",
    "            if data.iloc[i] >= 1:\n",
    "                if data.iloc[i-1] >=1:\n",
    "                    status.append('active')\n",
    "                else:\n",
    "                    if status[i-1] == 'noreg':\n",
    "                        status.append('new')\n",
    "                    else: \n",
    "                        status.append('return')\n",
    "            else:\n",
    "                if status[i-1] == 'noreg':\n",
    "                    status.append('noreg')\n",
    "                else: \n",
    "                    status.append('inactive')\n",
    "            \n",
    "    return pd.Series(status,pivot_user.columns)\n",
    "    \n",
    "type_table = pivot_user.apply(lambda x:judge_type(x),axis=1)\n",
    "print(type_table)\n",
    "type_res = type_table.replace('noreg',np.nan).apply(lambda x:x.value_counts())\n",
    "type_res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "eaf37c1e-0751-43ba-912c-44cac669991d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_res.T.plot.area(stacked=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b0b4fa8c-bbcd-4419-adb4-53d7e0c06b2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year_month</th>\n",
       "      <th>2016-08</th>\n",
       "      <th>2016-09</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005666</td>\n",
       "      <td>0.002283</td>\n",
       "      <td>0.001818</td>\n",
       "      <td>0.002890</td>\n",
       "      <td>0.002358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>0.003027</td>\n",
       "      <td>0.002977</td>\n",
       "      <td>0.002148</td>\n",
       "      <td>0.002018</td>\n",
       "      <td>0.001859</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>0.002907</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.001166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inactive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.407767</td>\n",
       "      <td>0.614458</td>\n",
       "      <td>0.606618</td>\n",
       "      <td>0.762040</td>\n",
       "      <td>0.796804</td>\n",
       "      <td>0.789091</td>\n",
       "      <td>0.787572</td>\n",
       "      <td>0.808962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919755</td>\n",
       "      <td>0.900116</td>\n",
       "      <td>0.899192</td>\n",
       "      <td>0.906482</td>\n",
       "      <td>0.927916</td>\n",
       "      <td>0.908215</td>\n",
       "      <td>0.913753</td>\n",
       "      <td>0.914535</td>\n",
       "      <td>0.922795</td>\n",
       "      <td>0.947423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.592233</td>\n",
       "      <td>0.379518</td>\n",
       "      <td>0.389706</td>\n",
       "      <td>0.229462</td>\n",
       "      <td>0.194064</td>\n",
       "      <td>0.203636</td>\n",
       "      <td>0.205202</td>\n",
       "      <td>0.183962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067979</td>\n",
       "      <td>0.088941</td>\n",
       "      <td>0.086559</td>\n",
       "      <td>0.081999</td>\n",
       "      <td>0.060528</td>\n",
       "      <td>0.078431</td>\n",
       "      <td>0.072291</td>\n",
       "      <td>0.073110</td>\n",
       "      <td>0.066486</td>\n",
       "      <td>0.045584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006024</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.002833</td>\n",
       "      <td>0.006849</td>\n",
       "      <td>0.005455</td>\n",
       "      <td>0.004335</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011756</td>\n",
       "      <td>0.007916</td>\n",
       "      <td>0.011272</td>\n",
       "      <td>0.009371</td>\n",
       "      <td>0.009538</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.011761</td>\n",
       "      <td>0.009448</td>\n",
       "      <td>0.009227</td>\n",
       "      <td>0.005828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year_month  2016-08   2016-09   2016-10   2016-11   2016-12   2017-01  \\\n",
       "active          NaN       NaN       NaN       NaN       NaN  0.005666   \n",
       "inactive        NaN  0.357143  0.407767  0.614458  0.606618  0.762040   \n",
       "new             1.0  0.642857  0.592233  0.379518  0.389706  0.229462   \n",
       "return          NaN       NaN       NaN  0.006024  0.003676  0.002833   \n",
       "\n",
       "year_month   2017-02   2017-03   2017-04   2017-05  ...   2018-05   2018-06  \\\n",
       "active      0.002283  0.001818  0.002890  0.002358  ...  0.000511  0.003027   \n",
       "inactive    0.796804  0.789091  0.787572  0.808962  ...  0.919755  0.900116   \n",
       "new         0.194064  0.203636  0.205202  0.183962  ...  0.067979  0.088941   \n",
       "return      0.006849  0.005455  0.004335  0.004717  ...  0.011756  0.007916   \n",
       "\n",
       "year_month   2018-07   2018-08   2018-09   2018-10   2018-11   2018-12  \\\n",
       "active      0.002977  0.002148  0.002018  0.001859  0.002195  0.002907   \n",
       "inactive    0.899192  0.906482  0.927916  0.908215  0.913753  0.914535   \n",
       "new         0.086559  0.081999  0.060528  0.078431  0.072291  0.073110   \n",
       "return      0.011272  0.009371  0.009538  0.011494  0.011761  0.009448   \n",
       "\n",
       "year_month   2019-01   2019-02  \n",
       "active      0.001493  0.001166  \n",
       "inactive    0.922795  0.947423  \n",
       "new         0.066486  0.045584  \n",
       "return      0.009227  0.005828  \n",
       "\n",
       "[4 rows x 31 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每月不同用户占比\n",
    "\n",
    "type_rate = type_res.apply(lambda x:x /x.sum())\n",
    "type_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e3555887-7c0b-49e3-b5cc-6293e2a0de24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_rate.T.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d3f309d9-a1cf-4608-a9a3-6ff89aa80876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#每月活跃用户占比\n",
    "\n",
    "type_rate.loc['active'].plot()\n",
    "plt.title('active user rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "64163696-c1ef-4588-bfca-2f04f6f24236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#每月回流用户占比\n",
    "\n",
    "type_rate.loc['return'].plot()\n",
    "plt.title('return user rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b1a29f27-c7ff-4b6a-b173-a84b73dd76ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.00736823576229978)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(type_rate.loc['return'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9b3d4a05-4183-4e89-bcd5-abd133a35ee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  author  date\n",
      "0                wenbiao   0.0\n",
      "1      Goh Yu Wen Eunice   0.0\n",
      "2               Hui Shan   0.0\n",
      "3                 Huihui   0.0\n",
      "4               KO-CHENG   0.0\n",
      "...                  ...   ...\n",
      "7717                  芊羽   0.0\n",
      "7718                  華山   0.0\n",
      "7719                   蘇   0.0\n",
      "7720                  郁君   0.0\n",
      "7721                  青慧   0.0\n",
      "\n",
      "[7722 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7722.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.073297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>98.699848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>864.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date\n",
       "count  7722.000000\n",
       "mean     23.073297\n",
       "std      98.699848\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       0.000000\n",
       "75%       0.000000\n",
       "max     864.000000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户生命周期分析 每一个用户最后一次购买商品的时间和第一次购买的时间差\n",
    "\n",
    "user_period = df.groupby(\"author\")['date'].apply(lambda x: x.max() - x.min())/np.timedelta64(1,'D')\n",
    "user_period = user_period.reset_index()\n",
    "print(user_period)\n",
    "user_period.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "ebac3303-2612-469f-b84f-d43360381465",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       0.0\n",
      "1       0.0\n",
      "2       0.0\n",
      "3       0.0\n",
      "4       0.0\n",
      "       ... \n",
      "7717    0.0\n",
      "7718    0.0\n",
      "7719    0.0\n",
      "7720    0.0\n",
      "7721    0.0\n",
      "Name: date, Length: 7722, dtype: float64\n",
      "date\n",
      "0.0      7130\n",
      "104.0       5\n",
      "372.0       5\n",
      "275.0       5\n",
      "156.0       4\n",
      "62.0        4\n",
      "221.0       4\n",
      "142.0       4\n",
      "302.0       4\n",
      "173.0       4\n",
      "388.0       3\n",
      "146.0       3\n",
      "224.0       3\n",
      "90.0        3\n",
      "80.0        3\n",
      "40.0        3\n",
      "283.0       3\n",
      "143.0       3\n",
      "158.0       3\n",
      "268.0       3\n",
      "348.0       3\n",
      "467.0       3\n",
      "368.0       3\n",
      "310.0       3\n",
      "472.0       3\n",
      "6.0         3\n",
      "10.0        3\n",
      "91.0        3\n",
      "234.0       3\n",
      "222.0       3\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(user_period['date'])\n",
    "\n",
    "#各个日期出现的次数\n",
    "life_time = user_period['date'].value_counts().sort_values(ascending=False)\n",
    "print(life_time.head(30))\n",
    "\n",
    "#绘制直方图 \n",
    "user_period['date'].plot.hist(bins=100,figsize=(12,6))\n",
    "\n",
    "#plt.ylim(0,1000) \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "4b063d11-da41-44cd-a366-d5c7eb8120f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300.9662162162162\n"
     ]
    }
   ],
   "source": [
    "#生命周期大于0的用户直方图\n",
    "#绘制直方图 \n",
    "user_period[user_period['date'] >0]['date'].plot.hist(bins=100,figsize=(12,6))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(user_period[user_period['date'] >0]['date'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "00c811e8-27d0-4198-ac85-e444293de0f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>date</th>\n",
       "      <th>date_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>wenbiao</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Goh Yu Wen Eunice</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hui Shan</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Huihui</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KO-CHENG</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>芊羽</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8753</th>\n",
       "      <td>華山</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8754</th>\n",
       "      <td>蘇</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>郁君</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>青慧</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8757 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  author   date  date_diff\n",
       "0                wenbiao 0 days        0.0\n",
       "1      Goh Yu Wen Eunice 0 days        0.0\n",
       "2               Hui Shan 0 days        0.0\n",
       "3                 Huihui 0 days        0.0\n",
       "4               KO-CHENG 0 days        0.0\n",
       "...                  ...    ...        ...\n",
       "8752                  芊羽 0 days        0.0\n",
       "8753                  華山 0 days        0.0\n",
       "8754                   蘇 0 days        0.0\n",
       "8755                  郁君 0 days        0.0\n",
       "8756                  青慧 0 days        0.0\n",
       "\n",
       "[8757 rows x 3 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户留存率 1-90天有多少留存用户\n",
    "#使用pd.cut函数\n",
    "#计算方式，每次消费时间\n",
    "\n",
    "user_contain = gp_user['date'].apply(lambda x: x-x.min()).reset_index()[['author','date']]\n",
    "user_contain['date_diff'] = user_contain['date']/np.timedelta64(1,'D')\n",
    "print(user_contain['author'].str)\n",
    "user_contain\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "7842a435-27ae-4a82-93f4-17f35a19a3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 90, 180, 270, 360, 450, 540, 630, 720, 810, 900]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成时间跨度，判断属于哪个区间\n",
    "\n",
    "bin = [ i *90  for i in range(11) ]\n",
    "bin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "2e3156a1-0804-4959-ab58-4560ca39cf22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>date</th>\n",
       "      <th>date_diff</th>\n",
       "      <th>date_diff_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8697</th>\n",
       "      <td>yuen ha</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8698</th>\n",
       "      <td>yuen ping michelle</td>\n",
       "      <td>127 days</td>\n",
       "      <td>127.0</td>\n",
       "      <td>(90.0, 180.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8699</th>\n",
       "      <td>yuen ping michelle</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8700</th>\n",
       "      <td>yuh-ming</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8701</th>\n",
       "      <td>yuhao</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8702</th>\n",
       "      <td>yuhuan</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8703</th>\n",
       "      <td>yuk wa</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8704</th>\n",
       "      <td>yuk yin</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8705</th>\n",
       "      <td>yuki</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8706</th>\n",
       "      <td>yukshan</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8707</th>\n",
       "      <td>yun chieh</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8708</th>\n",
       "      <td>yun han</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8709</th>\n",
       "      <td>yunching</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8710</th>\n",
       "      <td>yung min</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8711</th>\n",
       "      <td>yungi</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8712</th>\n",
       "      <td>yungyi</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8713</th>\n",
       "      <td>yunjeong</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8714</th>\n",
       "      <td>yunjin</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8715</th>\n",
       "      <td>yunmi</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8716</th>\n",
       "      <td>yunsuk</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8717</th>\n",
       "      <td>yunus</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8718</th>\n",
       "      <td>yvonne</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8719</th>\n",
       "      <td>zazah jane</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8720</th>\n",
       "      <td>zheng qiang</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8721</th>\n",
       "      <td>zheng wei</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8722</th>\n",
       "      <td>zhi hao</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8723</th>\n",
       "      <td>zhuang wei</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8724</th>\n",
       "      <td>ziv</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8725</th>\n",
       "      <td>ziyi</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8726</th>\n",
       "      <td>zona</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8727</th>\n",
       "      <td>zubi</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8728</th>\n",
       "      <td>中鴻</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8729</th>\n",
       "      <td>久茵</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8730</th>\n",
       "      <td>仰立</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8731</th>\n",
       "      <td>佳鈴</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8732</th>\n",
       "      <td>千瑞</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8733</th>\n",
       "      <td>受恩</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8734</th>\n",
       "      <td>嘉文</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8735</th>\n",
       "      <td>國俊</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8736</th>\n",
       "      <td>國隆</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8737</th>\n",
       "      <td>士齊</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8738</th>\n",
       "      <td>孟璇</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8739</th>\n",
       "      <td>宛庭</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8740</th>\n",
       "      <td>宜暹</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8741</th>\n",
       "      <td>建翔</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8742</th>\n",
       "      <td>志坤</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8743</th>\n",
       "      <td>怡婷</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8744</th>\n",
       "      <td>懿馨</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8745</th>\n",
       "      <td>承澎</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8746</th>\n",
       "      <td>振裕</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8747</th>\n",
       "      <td>曉君</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8748</th>\n",
       "      <td>洪</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8749</th>\n",
       "      <td>淑楨</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8750</th>\n",
       "      <td>素鳳</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8751</th>\n",
       "      <td>翔</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>芊羽</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8753</th>\n",
       "      <td>華山</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8754</th>\n",
       "      <td>蘇</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>郁君</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>青慧</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  author     date  date_diff date_diff_range\n",
       "8697             yuen ha   0 days        0.0             NaN\n",
       "8698  yuen ping michelle 127 days      127.0   (90.0, 180.0]\n",
       "8699  yuen ping michelle   0 days        0.0             NaN\n",
       "8700            yuh-ming   0 days        0.0             NaN\n",
       "8701               yuhao   0 days        0.0             NaN\n",
       "8702              yuhuan   0 days        0.0             NaN\n",
       "8703              yuk wa   0 days        0.0             NaN\n",
       "8704             yuk yin   0 days        0.0             NaN\n",
       "8705                yuki   0 days        0.0             NaN\n",
       "8706             yukshan   0 days        0.0             NaN\n",
       "8707           yun chieh   0 days        0.0             NaN\n",
       "8708             yun han   0 days        0.0             NaN\n",
       "8709            yunching   0 days        0.0             NaN\n",
       "8710            yung min   0 days        0.0             NaN\n",
       "8711               yungi   0 days        0.0             NaN\n",
       "8712              yungyi   0 days        0.0             NaN\n",
       "8713            yunjeong   0 days        0.0             NaN\n",
       "8714              yunjin   0 days        0.0             NaN\n",
       "8715               yunmi   0 days        0.0             NaN\n",
       "8716              yunsuk   0 days        0.0             NaN\n",
       "8717               yunus   0 days        0.0             NaN\n",
       "8718              yvonne   0 days        0.0             NaN\n",
       "8719          zazah jane   0 days        0.0             NaN\n",
       "8720         zheng qiang   0 days        0.0             NaN\n",
       "8721           zheng wei   0 days        0.0             NaN\n",
       "8722             zhi hao   0 days        0.0             NaN\n",
       "8723          zhuang wei   0 days        0.0             NaN\n",
       "8724                 ziv   0 days        0.0             NaN\n",
       "8725                ziyi   0 days        0.0             NaN\n",
       "8726                zona   0 days        0.0             NaN\n",
       "8727                zubi   0 days        0.0             NaN\n",
       "8728                  中鴻   0 days        0.0             NaN\n",
       "8729                  久茵   0 days        0.0             NaN\n",
       "8730                  仰立   0 days        0.0             NaN\n",
       "8731                  佳鈴   0 days        0.0             NaN\n",
       "8732                  千瑞   0 days        0.0             NaN\n",
       "8733                  受恩   0 days        0.0             NaN\n",
       "8734                  嘉文   0 days        0.0             NaN\n",
       "8735                  國俊   0 days        0.0             NaN\n",
       "8736                  國隆   0 days        0.0             NaN\n",
       "8737                  士齊   0 days        0.0             NaN\n",
       "8738                  孟璇   0 days        0.0             NaN\n",
       "8739                  宛庭   0 days        0.0             NaN\n",
       "8740                  宜暹   0 days        0.0             NaN\n",
       "8741                  建翔   0 days        0.0             NaN\n",
       "8742                  志坤   0 days        0.0             NaN\n",
       "8743                  怡婷   0 days        0.0             NaN\n",
       "8744                  懿馨   0 days        0.0             NaN\n",
       "8745                  承澎   0 days        0.0             NaN\n",
       "8746                  振裕   0 days        0.0             NaN\n",
       "8747                  曉君   0 days        0.0             NaN\n",
       "8748                   洪   0 days        0.0             NaN\n",
       "8749                  淑楨   0 days        0.0             NaN\n",
       "8750                  素鳳   0 days        0.0             NaN\n",
       "8751                   翔   0 days        0.0             NaN\n",
       "8752                  芊羽   0 days        0.0             NaN\n",
       "8753                  華山   0 days        0.0             NaN\n",
       "8754                   蘇   0 days        0.0             NaN\n",
       "8755                  郁君   0 days        0.0             NaN\n",
       "8756                  青慧   0 days        0.0             NaN"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_contain['date_diff_range'] = pd.cut(user_contain['date_diff'],bins=bin,include_lowest=true)\n",
    "user_contain.tail(60)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python(Data_clean)",
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